From d4d87f2371886ccd7c0ff9e8207442bc75f92772 Mon Sep 17 00:00:00 2001 From: Jorge Beltran Date: Wed, 13 Jan 2021 12:47:12 +0100 Subject: [PATCH] v2.0 release. Added support for monocular cameras and multi-pose calibration. --- .gitignore | 3 +- CMakeLists.txt | 45 +- HOWTO.md | 96 ++ README.md | 96 +- cfg/{Laser.cfg => Lidar.cfg} | 6 +- cfg/Monocular.cfg | 14 + cfg/{Camera.cfg => Stereo.cfg} | 3 +- include/velo2cam_utils.h | 462 +++++--- launch/fake_tf.launch | 6 - launch/full_calibration.launch | 5 - launch/laser_pattern.launch | 47 - launch/levinson.launch | 12 - launch/levinson_edges.launch | 28 - launch/lidar_pattern.launch | 53 + launch/mono_pattern.launch | 19 + launch/pcl_coloring.launch | 9 - launch/pcl_projection.launch | 9 - launch/real_laser_pattern.launch | 47 - launch/real_stereo_pattern.launch | 138 --- launch/registration.launch | 17 + launch/stereo_pattern.launch | 119 +- launch/velo2cam_calibration.launch | 10 - ...calibration_target_real_scheme_journal.png | Bin 0 -> 333194 bytes screenshots/calibration_target_scheme.png | Bin 191060 -> 0 bytes screenshots/coordinates_frames.png | Bin 22538 -> 0 bytes screenshots/lidar_filters_1.png | Bin 0 -> 345337 bytes screenshots/lidar_filters_2.png | Bin 0 -> 304065 bytes screenshots/real_results.png | Bin 0 -> 1834866 bytes screenshots/stereo_filters_1.png | Bin 0 -> 291586 bytes screenshots/stereo_filters_2.png | Bin 0 -> 295408 bytes screenshots/velo2cam_calibration_setup.png | Bin 1625226 -> 0 bytes src/disp_masker.cpp | 112 +- src/laser_pattern.cpp | 474 -------- src/levinson.cpp | 574 --------- src/lidar_pattern.cpp | 570 +++++++++ src/mono_qr_pattern.cpp | 599 ++++++++++ src/pcl_coloring.cpp | 130 --- src/pcl_projection.cpp | 209 ---- src/plane.cpp | 101 +- src/stereo_pattern.cpp | 614 +++++----- src/velo2cam_calibration.cpp | 1031 +++++++++++------ 41 files changed, 2876 insertions(+), 2782 deletions(-) create mode 100644 HOWTO.md rename cfg/{Laser.cfg => Lidar.cfg} (86%) create mode 100755 cfg/Monocular.cfg rename cfg/{Camera.cfg => Stereo.cfg} (62%) delete mode 100644 launch/fake_tf.launch delete mode 100644 launch/full_calibration.launch delete mode 100644 launch/laser_pattern.launch delete mode 100644 launch/levinson.launch delete mode 100644 launch/levinson_edges.launch create mode 100644 launch/lidar_pattern.launch create mode 100644 launch/mono_pattern.launch delete mode 100644 launch/pcl_coloring.launch delete mode 100644 launch/pcl_projection.launch delete mode 100644 launch/real_laser_pattern.launch delete mode 100644 launch/real_stereo_pattern.launch create mode 100644 launch/registration.launch delete mode 100644 launch/velo2cam_calibration.launch create mode 100644 screenshots/calibration_target_real_scheme_journal.png delete mode 100644 screenshots/calibration_target_scheme.png delete mode 100644 screenshots/coordinates_frames.png create mode 100644 screenshots/lidar_filters_1.png create mode 100644 screenshots/lidar_filters_2.png create mode 100644 screenshots/real_results.png create mode 100644 screenshots/stereo_filters_1.png create mode 100644 screenshots/stereo_filters_2.png delete mode 100644 screenshots/velo2cam_calibration_setup.png delete mode 100644 src/laser_pattern.cpp delete mode 100644 src/levinson.cpp create mode 100644 src/lidar_pattern.cpp create mode 100644 src/mono_qr_pattern.cpp delete mode 100644 src/pcl_coloring.cpp delete mode 100644 src/pcl_projection.cpp diff --git a/.gitignore b/.gitignore index b7fd55b..14a6436 100644 --- a/.gitignore +++ b/.gitignore @@ -23,4 +23,5 @@ srv_gen/ launch/calibrated_tf* scripts/ -src/mono_pattern.cpp +.idea/ +.vscode/ \ No newline at end of file diff --git a/CMakeLists.txt b/CMakeLists.txt index 7c301d9..9db2aac 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,7 +1,7 @@ cmake_minimum_required(VERSION 2.8.3) project(velo2cam_calibration) -SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -w ") +SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -w ") ## Find catkin macros and libraries find_package(catkin REQUIRED COMPONENTS @@ -18,19 +18,16 @@ find_package(catkin REQUIRED COMPONENTS tf_conversions dynamic_reconfigure cmake_modules - # geometry_msgs - # tf2 - # tf2_ros - # tf2_sensor_msgs ) find_package(TinyXML REQUIRED) -find_package(OpenCV 3 REQUIRED) +find_package(OpenCV REQUIRED) find_package(PCL 1.7 REQUIRED) generate_dynamic_reconfigure_options( - cfg/Laser.cfg - cfg/Camera.cfg + cfg/Lidar.cfg + cfg/Stereo.cfg + cfg/Monocular.cfg cfg/Plane.cfg ) @@ -78,18 +75,16 @@ add_definitions( ) ## Declare cpp executables -add_executable(laser_pattern src/laser_pattern.cpp) +add_executable(lidar_pattern src/lidar_pattern.cpp) add_executable(stereo_pattern src/stereo_pattern.cpp) +add_executable(mono_qr_pattern src/mono_qr_pattern.cpp) add_executable(velo2cam_calibration src/velo2cam_calibration.cpp) -add_executable(pcl_coloring src/pcl_coloring.cpp) -add_executable(pcl_projection src/pcl_projection.cpp) -add_executable(levinson src/levinson.cpp) add_executable(v2c_plane_segmentation src/plane.cpp) add_executable(v2c_disp_masker src/disp_masker.cpp) ## Add cmake target dependencies of the executable/library ## as an example, message headers may need to be generated before nodes -add_dependencies(laser_pattern +add_dependencies(lidar_pattern ${catkin_EXPORTED_TARGETS} velo2cam_calibration_generate_messages_cpp ${PROJECT_NAME}_gencfg @@ -99,13 +94,17 @@ add_dependencies(stereo_pattern velo2cam_calibration_generate_messages_cpp ${PROJECT_NAME}_gencfg ) +add_dependencies(mono_qr_pattern + ${catkin_EXPORTED_TARGETS} + velo2cam_calibration_generate_messages_cpp + ${PROJECT_NAME}_gencfg +) add_dependencies( velo2cam_calibration ${catkin_EXPORTED_TARGETS} velo2cam_calibration_generate_messages_cpp ${PROJECT_NAME}_gencfg ) -add_dependencies(levinson ${catkin_EXPORTED_TARGETS}) add_dependencies(v2c_plane_segmentation ${PROJECT_NAME}_gencfg) ## Specify libraries to link a library or executable target against @@ -114,31 +113,21 @@ target_link_libraries(stereo_pattern ${OpenCV_LIBRARIES} ${PCL_LIBRARIES} ) -target_link_libraries(laser_pattern - ${catkin_LIBRARIES} - ${OpenCV_LIBRARIES} - ${PCL_LIBRARIES} -) -target_link_libraries(velo2cam_calibration - ${catkin_LIBRARIES} - ${OpenCV_LIBRARIES} - ${PCL_LIBRARIES} - ${TinyXML_LIBRARIES} -) -target_link_libraries(pcl_coloring +target_link_libraries(lidar_pattern ${catkin_LIBRARIES} ${OpenCV_LIBRARIES} ${PCL_LIBRARIES} ) -target_link_libraries(pcl_projection +target_link_libraries(mono_qr_pattern ${catkin_LIBRARIES} ${OpenCV_LIBRARIES} ${PCL_LIBRARIES} ) -target_link_libraries(levinson +target_link_libraries(velo2cam_calibration ${catkin_LIBRARIES} ${OpenCV_LIBRARIES} ${PCL_LIBRARIES} + ${TinyXML_LIBRARIES} ) target_link_libraries(v2c_plane_segmentation ${catkin_LIBRARIES} diff --git a/HOWTO.md b/HOWTO.md new file mode 100644 index 0000000..db4a353 --- /dev/null +++ b/HOWTO.md @@ -0,0 +1,96 @@ +# User guide: How to calibrate your sensors +*Velo2cam_calibration* is a tool to obtain the extrinsic parameters expressing the relative pose of a pair of sensors. Each of these sensors can be a monocular camera, a stereo camera, or a lidar scanner. + +# Setup # +First, you must identify the topics delivered by the drivers of the sensors to be calibrated. In particular, velo2cam_calibration expects the following inputs: +* Monocular: a rectified image ([sensor_msgs/Image](http://docs.ros.org/api/sensor_msgs/html/msg/Image.html)) and the corresponding meta-information topic ([sensor_msgs/CameraInfo](http://docs.ros.org/api/sensor_msgs/html/msg/CameraInfo.html)). Both topics are expected to be referred to the same *frame_id*. +* Stereo: a pair of rectified images (2x [sensor_msgs/Image](http://docs.ros.org/api/sensor_msgs/html/msg/Image.html)) and the corresponding meta-information topic ([sensor_msgs/CameraInfo](http://docs.ros.org/api/sensor_msgs/html/msg/CameraInfo.html)). All the topics are expected to be referred to the same *frame_id*. +* LiDAR: a 3D point cloud ([sensor_msgs/PointCloud2](http://docs.ros.org/api/sensor_msgs/html/msg/PointCloud2.html)). + +To start the calibration procedure, the reference point extraction nodes must be run. To that end, you can use the launch files included in this same repository. Arguments are available to pass the topic and frame names to the nodes. A different launch file must be used for each sensor, and it will be different depending on its modality: + +* Monocular: *mono_pattern.launch*. Arguments to be set: + * *camera_name*: camera namespace in which the image topic and the meta-information topics exist. + * *image_topic*: name of the image topic excluding the camera namespace (e.g., *image_rect_color*). + * *frame_name*: *frame_id* to which camera topics are referred to. +* Stereo: *stereo_pattern.launch*. Arguments to be set: + * *camera_name*: camera namespace. Images of the stereo pair and meta-information topics should exist under */left* and */right*. + * *image_topic*: name of the image topics under their respective namespaces (e.g., *image_rect_color*). + * *frame_name*: *frame_id* of the left image. +* LiDAR: *lidar_pattern.launch*. Arguments to be set: + * *cloud_topic*: LiDAR point cloud topic. + +In all cases, if the two sensors to be calibrated belong to the same modality, an additional argument *sensor_id* must be set so that one of the devices is given *sensor_id=0* and the other, *sensor_id=1*. + +Apart from this, a third launch file must be run to perform the registration step: +* Registration: *registration.launch*. Arguments to be set: + * *sensor1_type* and *sensor2_type*: modalities of the sensors to be calibrated, to be chosen from *mono*, *stereo*, and *lidar*. + * *sensor1_id* and *sensor2_id*: to be set to 0 and 1, respectively, only if the two devices to be calibrated use the same modality. + +## Examples ## +For instance, if you want to calibrate a monocular camera delivering the topics */blackflys/image_rect_color* (rectified_image) and */blackflys/camera_info* (meta-information), both referred to the *blackflys* *frame_id*, and a LiDAR scanner providing a */hdl64_points* cloud, you should run the following commands in three separate terminals: + +```roslaunch velo2cam_calibration mono_pattern.launch camera_name:=/blackflys image_topic:=image_rect_color frame_name:=blackflys``` + +```roslaunch velo2cam_calibration lidar_pattern.launch cloud_topic:=/hdl64_points``` + +```roslaunch velo2cam_calibration registration.launch sensor1_type:=mono sensor2_type:=lidar``` + + +On the other hand, if a stereo camera is involved, calibration can be performed using the monocular procedure on the left image of the pair. However, there also exists the possibility to use the specific stereo-based reference point extraction. If the stereo camera is publishing images in the */bumblebee_xb3/left/image_rect_color* and */bumblebee_xb3/left/image_rect_color* topics (with *frame_id* *bumblebee_xb3*), the launch files to use are: + +```roslaunch velo2cam_calibration stereo_pattern.launch camera_name:=/bumblebee_xb3 image_topic:=image_rect_color frame_name:=bumblebee_xb3``` + +```roslaunch velo2cam_calibration lidar_pattern.launch cloud_topic:=/hdl64_points``` + +```roslaunch velo2cam_calibration registration.launch sensor1_type:=stereo sensor2_type:=lidar``` + +Finally, if the sensors that will be calibrated are two LiDAR scanners, publishing */hdl64_points* and */hdl32_points* clouds, the *sensor_id* arguments must be set appropriately: + +```roslaunch velo2cam_calibration lidar_pattern.launch cloud_topic:=/hdl64_points sensor_id:=0``` + +```roslaunch velo2cam_calibration lidar_pattern.launch cloud_topic:=/hdl32_points sensor_id:=1``` + +```roslaunch velo2cam_calibration registration.launch sensor1_type:=lidar sensor2_type:=lidar sensor1_id:=0 sensor2_id:=1``` + +# Warm-up stage # +The instructions to perform the calibration will appear on the terminal where the registration launch file has been run. In the beginning, there is a warm-up stage where the user must check that the calibration pattern is correctly represented in the data from both sensors. The procedure takes place sequentially (one sensor after the other), and there are two tasks to be performed: one, to make sure that the pattern is within the field of view of the sensors; and two, to adjust the passthrough filters intended to prevent outliers in range data. Any sensor data visualization tool can be used at this point, although *rviz* is encouraged. Passthrough filters can be modified through *dynamic_reconfigure*. In a typical use case, the following GUIs can be launched in parallel: + +```rviz``` + +```rosrun rqt_reconfigure rqt_reconfigure``` + +*rviz* will be used to visualize the images and/or point clouds, and *rqt_reconfigure* to modify the filter parameters on the fly. Note that the registration node only will let the user advance when the pattern can be identified in the current sensor. If the calibration pattern needs to be moved, the procedure should be reset so that the adequacy of the new location is rechecked. + +For monocular devices, the only relevant data is the image, where the pattern should be fully visible. For range data, the checklist depends on the modality. + +## Stereo ## +The four pattern circles must be well visible in */z_filtered_cloud*. Otherwise, please modify the limits of the passthrough filter of the *full_pass_through_z* node in rqt_reconfigure (parameters *filter_limit_min* and *filter_limit_max*) so that they represent the area where the pattern is located. Then, apply the same values to the *edges_pass_through_z* node. + +As an example, please check the following image, where the default passthrough values are not suitable: + +![gazebo screenshot](screenshots/stereo_filters_1.png) + +After the *filter_limit_max* variable has been conveniently enlarged to 4 meters, the pattern becomes visible: + +![gazebo screenshot](screenshots/stereo_filters_2.png) + +## LiDAR ## +The *lidar_pattern_/zyx_filtered* cloud should represent only the calibration pattern and the points corresponding to those lidar rays that go through the holes. The cloud can be filtered through the parameters *filter_limit_min* and *filter_limit_max* of the *pass_through_x_velo_*, *pass_through_y_velo_*, and *pass_through_z_velo_* nodes. + +On the other hand, *lidar_pattern_/range_filtered_cloud* must contain only the calibration pattern. To that end, a radial passthrough filter is available in the *lidar_pattern_* node, tunable through the *passthrough_radius_min* and *passthrough_radius_max* parameters. + +Please check the following example, where the *lidar_pattern_/zyx_filtered* cloud is depicted in white and *lidar_pattern_/range_filtered_cloud*, in red. The initial parameters yield the following clouds: + +![gazebo screenshot](screenshots/lidar_filters_1.png) + +Ideally, the parameters should be set so that the clouds are close to the following: + +![gazebo screenshot](screenshots/lidar_filters_2.png) + +Note, however, that the filters do not need an exact tuning as long as the areas of interest (i.e., the pattern and the surface behind it) are well defined in the LiDAR clouds. + +## Calibration ## +When the pattern is visible in all the modalities, and the passthrough filters have been adjusted, the user can start the calibration. It is important to make sure that the scene is static during the whole process, which will require 30 frames where the calibration pattern is correctly identified. Some errors might be shown in the terminals where the reference point extraction launch files were run; it is safe to ignore them as long as the iteration count provided by the registration node increases. At the end of the calibration, the program will ask the user if a new pattern pose is needed. It is recommended to repeat the procedure at least three times, each with a different position and orientation of the calibration pattern. Each new iteration starts with the same warm-up stage described above, where the passthrough filters should be properly adjusted to the new pattern location. + +Calibration can be finished after any iteration. The final result will be immediately displayed, and a calibrated_tf.launch file will be created in the *launch* folder within the *velo2cam_calibration* package path, containing the *static_transform_publisher* able to broadcast the correct *tf* transformation. \ No newline at end of file diff --git a/README.md b/README.md index 78761a2..e8aeb5d 100644 --- a/README.md +++ b/README.md @@ -1,92 +1,34 @@ # velo2cam_calibration [![Build Status](http://build.ros.org/job/Kdev__velo2cam_calibration__ubuntu_xenial_amd64/badge/icon)](http://build.ros.org/job/Kdev__velo2cam_calibration__ubuntu_xenial_amd64/) -The *velo2cam_calibration* software implements an Automatic Calibration algorithm for Lidar-Stereo camera setups \[1\]. This software is provided as a ROS package. +The *velo2cam_calibration* software implements a state-of-the-art automatic calibration algorithm for pair of sensors composed of LiDAR and camera devices in any possible combination \[1\]. This software is provided as a ROS package. Package developed at [Intelligent Systems Laboratory](http://www.uc3m.es/islab), Universidad Carlos III de Madrid. -![gazebo screenshot](screenshots/velo2cam_calibration_setup.png) +![real results](screenshots/real_results.png) -# Nodes # -## stereo_pattern ## -### Subscribed Topics ### -*cloud2* ([sensor_msgs/PointCloud2](http://docs.ros.org/api/sensor_msgs/html/msg/PointCloud2.html)) +## Setup ## +To install this ROS package: +1. Clone the repository into your *catkin_ws/src/* folder. +2. Install run dependencies: ```sudo apt-get install ros--opencv-apps``` +3. Build your workspace [as usual](http://wiki.ros.org/ROS/Tutorials/BuildingPackages). -    Stereo camera point cloud containing points belonging to edges in the left image +## Usage ## +See [HOWTO.md](HOWTO.md) for detailed instructions on how to use this software. -*cam_plane_coeffs* ([pcl_msgs::ModelCoefficients](http://docs.ros.org/api/pcl_msgs/html/msg/ModelCoefficients.html)) +To test the algorithm in a virtual environment, you can launch any of the calibration scenarios included in our [Gazebo validation suite](https://github.com/beltransen/velo2cam_gazebo). -    Coefficients of the calibration target plane model -### Published Topics ### -*/stereo_pattern/centers_cloud* ([velo2cam_calibration::ClusterCentroids](http://docs.ros.org/kinetic/api/velo2cam_calibration/html/msg/ClusterCentroids.html)) +## Calibration target ## +The following picture shows a possible embodiment of the proposed calibration target used by this algorithm and its corresponding dimensional drawing. -    Target circles centers obtained from stereo camera data +![calibration target](screenshots/calibration_target_real_scheme_journal.png) - -## laser_pattern ## -### Subscribed Topics ### -*cloud1* ([sensor_msgs/PointCloud2](http://docs.ros.org/api/sensor_msgs/html/msg/PointCloud2.html)) +**Note:** Other size may be used for convenience. If so, please configure node parameters accordingly. -    LIDAR pointcloud -### Published Topics ### -*/laser_pattern/centers_cloud* ([velo2cam_calibration::ClusterCentroids](http://docs.ros.org/kinetic/api/velo2cam_calibration/html/msg/ClusterCentroids.html)) +## Citation ## +If you use this work in your research, please consider citing the following paper: -    Target circles centers obtained from LIDAR data - -## velo2cam_calibration ## -### Subscribed Topics ### -*~cloud1* ([velo2cam_calibration::ClusterCentroids](http://docs.ros.org/kinetic/api/velo2cam_calibration/html/msg/ClusterCentroids.html)) +[1] Beltrán, J., Guindel, C., and García, F. (2021). [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://arxiv.org/abs/2101.04431). arXiv:2101.04431 [cs.CV]. Submitted to IEEE Transactions on Intelligent Transportation Systems. -    Target circles centers obtained from LIDAR data +A previous version of this tool is available [here](https://github.com/beltransen/velo2cam_calibration/tree/v1.0) and was described on this paper: -*~cloud2* ([velo2cam_calibration::ClusterCentroids](http://docs.ros.org/kinetic/api/velo2cam_calibration/html/msg/ClusterCentroids.html)) - -    Target circles centers obtained from stereo camera data - -*~cloud3* ([sensor_msgs/PointCloud2](http://docs.ros.org/api/sensor_msgs/html/msg/PointCloud2.html)) - -    Original LIDAR pointcloud -### Published Topics ### -TF containing the transformation between both sensors ([see TF info in ROS Wiki](http://wiki.ros.org/tf)) - -The node broadcasts the TF transformation between *velodyne* and *stereo* frames. -The fixed transformation between *stereo_camera* and *stereo* is published by a static broadcaster in *stereo_pattern.launch*. -The picture below shows the three coordinate frames: - -![gazebo screenshot](screenshots/coordinates_frames.png) - -**Note**: Additionally, a .launch file called *calibrated_tf.launch* containing the proper TF broadcasters is created in the */launch* folder of the *velo2cam_calibration* package so -you can use the calibration just executing: - -```roslaunch velo2cam_calibration calibrated_tf.launch``` - -### Parameters ### -As described in the paper, different parameters can be selected. The ones that you will usually need are set in the launch files. For convenience, two sets of launch files are provided, with different parameters intended for the Gazebo environment, on the one hand, or the real wood pattern, on the other hand. The latter have *real_* as a prefix. - -# Usage # -Some sample .launch files are provided in this package. The simplest way to launch the algorithm is by running the three main ROS nodes as follows: - -```roslaunch velo2cam_calibration laser_pattern.launch``` - -```roslaunch velo2cam_calibration stereo_pattern.launch``` - -```roslaunch velo2cam_calibration velo2cam_calibration.launch``` - -We also provide a launch file containing the three launch files above: - -```roslaunch velo2cam_calibration full_calibration.launch``` - -In order to test the algorithm, you can download an example .bag file [here](https://www.dropbox.com/s/fdvew31azdp9cbg/1_trans_0.bag?dl=1). - -**Note**: The .bag file above was generated using a simulator environment in Gazebo which is provided [here](https://github.com/beltransen/velo2cam_gazebo). We use the simulator in order to test the algorithm with a proper ground truth. - -# Calibration target details # -The following scheme shows the real size of the calibration target used by this algorithm. Measurements are given in centimeters (cm). - -![gazebo screenshot](screenshots/calibration_target_scheme.png) - -**Note:** Other size may be used for convenience. If so, please configure nodes parameters accordingly. - -# Citation # -\[1\] Guindel, C., Beltrán, J., Martín, D. and García, F. (2017). Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups. *IEEE International Conference on Intelligent Transportation Systems (ITSC), 674–679*. - -Pre-print available [here](https://arxiv.org/abs/1705.04085). +[2] Guindel, C., Beltrán, J., Martín, D., and García, F. (2017). [Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setup](https://arxiv.org/abs/1705.04085). *IEEE International Conference on Intelligent Transportation Systems (ITSC), 674–679*. \ No newline at end of file diff --git a/cfg/Laser.cfg b/cfg/Lidar.cfg similarity index 86% rename from cfg/Laser.cfg rename to cfg/Lidar.cfg index c5ddfe0..dba9f92 100755 --- a/cfg/Laser.cfg +++ b/cfg/Lidar.cfg @@ -10,11 +10,11 @@ gen = ParameterGenerator() gen.add("x", double_t, 0, "x coord", 0, 0, 1) gen.add("y", double_t, 0, "y coord", 0, 0, 1) gen.add("z", double_t, 0, "z coord", 1, 0, 1) -gen.add("angle_threshold", double_t, 0, "Angle threshold for plane segmentation", 0.35, 0, pi/2) +gen.add("angle_threshold", double_t, 0, "Angle threshold for plane segmentation", 0.55, 0, pi/2) gen.add("circle_radius", double_t, 0, "Radius of pattern's circles", 0.12, 0, 1) gen.add("passthrough_radius_min", double_t, 0, "Min radius for passthrough", 1.0, 0, 10) gen.add("passthrough_radius_max", double_t, 0, "Max radius for passthrough", 6.0, 0, 10) gen.add("centroid_distance_min", double_t, 0, "Min distance to the centroid", 0.15, 0.0, 1.0) -gen.add("centroid_distance_max", double_t, 0, "Max distance to the centroid", 0.25, 0.0, 1.0) +gen.add("centroid_distance_max", double_t, 0, "Max distance to the centroid", 0.8, 0.0, 1.0) -exit(gen.generate(PACKAGE, "velo2cam_calibration", "Laser")) +exit(gen.generate(PACKAGE, "velo2cam_calibration", "Lidar")) diff --git a/cfg/Monocular.cfg b/cfg/Monocular.cfg new file mode 100755 index 0000000..1b25f41 --- /dev/null +++ b/cfg/Monocular.cfg @@ -0,0 +1,14 @@ +#!/usr/bin/env python +PACKAGE = "velo2cam_calibration" + +from math import pi + +from dynamic_reconfigure.parameter_generator_catkin import * + +gen = ParameterGenerator() + +gen.add("marker_size", double_t, 0, "Size of the marker (m)", 0.2, 0.1, 0.5) +gen.add("delta_width_qr_center", double_t, 0, "width increment from target center to qr center (m)", 0.55, 0, 1) +gen.add("delta_height_qr_center", double_t, 0, "height increment from target center to qr center (m)", 0.35, 0, 1) + +exit(gen.generate(PACKAGE, "velo2cam_calibration", "Monocular")) diff --git a/cfg/Camera.cfg b/cfg/Stereo.cfg similarity index 62% rename from cfg/Camera.cfg rename to cfg/Stereo.cfg index cd85b19..53cc128 100755 --- a/cfg/Camera.cfg +++ b/cfg/Stereo.cfg @@ -6,6 +6,5 @@ from dynamic_reconfigure.parameter_generator_catkin import * gen = ParameterGenerator() gen.add("circle_threshold", double_t, 0, "Cam circle threshold", 0.01, 0, 5) -gen.add("line_threshold", double_t, 0, "Cam line threshold", 0.02, 0, 5) -exit(gen.generate(PACKAGE, "velo2cam_calibration", "Camera")) +exit(gen.generate(PACKAGE, "velo2cam_calibration", "Stereo")) diff --git a/include/velo2cam_utils.h b/include/velo2cam_utils.h index bea2b78..5943c50 100644 --- a/include/velo2cam_utils.h +++ b/include/velo2cam_utils.h @@ -1,6 +1,7 @@ /* - velo2cam_calibration - Automatic calibration algorithm for extrinsic parameters of a stereo camera and a velodyne - Copyright (C) 2017-2018 Jorge Beltran, Carlos Guindel + velo2cam_calibration - Automatic calibration algorithm for extrinsic + parameters of a stereo camera and a velodyne Copyright (C) 2017-2021 Jorge + Beltran, Carlos Guindel This file is part of velo2cam_calibration. @@ -18,208 +19,367 @@ along with velo2cam_calibration. If not, see . */ +/* + velo2cam_utils: Helper functions +*/ + #ifndef velo2cam_utils_H #define velo2cam_utils_H #define PCL_NO_PRECOMPILE #define DEBUG 0 -#include -#include -#include -#include -#include -#include +#include #include -#include -#include -#include -using namespace std; +#define TARGET_NUM_CIRCLES 4 +#define TARGET_RADIUS 0.12 +#define GEOMETRY_TOLERANCE 0.06 -static const int RINGS_COUNT = 16; // TODO AS FUNCTION PARAM +using namespace std; namespace Velodyne { - struct Point - { - PCL_ADD_POINT4D; // quad-word XYZ - float intensity; ///< laser intensity reading - uint16_t ring; ///< laser ring number - float range; - EIGEN_MAKE_ALIGNED_OPERATOR_NEW // ensure proper alignment - }EIGEN_ALIGN16; - - void addRange(pcl::PointCloud & pc){ - for (pcl::PointCloud::iterator pt = pc.points.begin(); pt < pc.points.end(); pt++) - { - pt->range = sqrt(pt->x * pt->x + pt->y * pt->y + pt->z * pt->z); - } +struct Point { + PCL_ADD_POINT4D; // quad-word XYZ + float intensity; ///< laser intensity reading + std::uint16_t ring; ///< laser ring number + float range; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW // ensure proper alignment +} EIGEN_ALIGN16; + +void addRange(pcl::PointCloud &pc) { + for (pcl::PointCloud::iterator pt = pc.points.begin(); + pt < pc.points.end(); pt++) { + pt->range = sqrt(pt->x * pt->x + pt->y * pt->y + pt->z * pt->z); } +} - vector > getRings(pcl::PointCloud & pc) - { - vector > rings(RINGS_COUNT); - for (pcl::PointCloud::iterator pt = pc.points.begin(); pt < pc.points.end(); pt++) - { - rings[pt->ring].push_back(&(*pt)); - } - return rings; +vector> getRings(pcl::PointCloud &pc, + int rings_count) { + vector> rings(rings_count); + for (pcl::PointCloud::iterator pt = pc.points.begin(); + pt < pc.points.end(); pt++) { + rings[pt->ring].push_back(&(*pt)); } + return rings; +} - // all intensities to range min-max - void normalizeIntensity(pcl::PointCloud & pc, float minv, float maxv) - { - float min_found = INFINITY; - float max_found = -INFINITY; +// all intensities to range min-max +void normalizeIntensity(pcl::PointCloud &pc, float minv, float maxv) { + float min_found = INFINITY; + float max_found = -INFINITY; - for (pcl::PointCloud::iterator pt = pc.points.begin(); pt < pc.points.end(); pt++) - { - max_found = max(max_found, pt->intensity); - min_found = min(min_found, pt->intensity); - } + for (pcl::PointCloud::iterator pt = pc.points.begin(); + pt < pc.points.end(); pt++) { + max_found = max(max_found, pt->intensity); + min_found = min(min_found, pt->intensity); + } - for (pcl::PointCloud::iterator pt = pc.points.begin(); pt < pc.points.end(); pt++) - { - pt->intensity = (pt->intensity - min_found) / (max_found - min_found) * (maxv - minv) + minv; - } + for (pcl::PointCloud::iterator pt = pc.points.begin(); + pt < pc.points.end(); pt++) { + pt->intensity = + (pt->intensity - min_found) / (max_found - min_found) * (maxv - minv) + + minv; } } +} // namespace Velodyne + +POINT_CLOUD_REGISTER_POINT_STRUCT(Velodyne::Point, + (float, x, x)(float, y, y)(float, z, z)( + float, intensity, + intensity)(std::uint16_t, ring, + ring)(float, range, range)); + +void sortPatternCenters(pcl::PointCloud::Ptr pc, + vector &v) { + // 0 -- 1 + // | | + // 3 -- 2 + + if (v.empty()) { + v.resize(4); + } -POINT_CLOUD_REGISTER_POINT_STRUCT( - Velodyne::Point, (float, x, x) (float, y, y) (float, z, z) - (float, intensity, intensity) (uint16_t, ring, ring) (float, range, range)); - -void sortPatternCentersXY(pcl::PointCloud::Ptr pc, vector &v){ - double avg_x = 0, avg_y = 0; - for(pcl::PointCloud::iterator it=pc->points.begin(); itpoints.end(); it++){ - avg_x += (*it).x; - avg_y += (*it).y; - } - - pcl::PointXYZ center; - center.x = avg_x/4.; - center.y = avg_y/4.; - - for(pcl::PointCloud::iterator it=pc->points.begin(); itpoints.end(); it++){ - double x_dif = (*it).x - center.x; - double y_dif = (*it).y - center.y; - - if(x_dif < 0 && y_dif < 0){ - v[0] = (*it); - }else if(x_dif > 0 && y_dif < 0){ - v[1] = (*it); - }else if(x_dif > 0 && y_dif > 0){ - v[2] = (*it); - }else{ - v[3] = (*it); + // Transform points to polar coordinates + pcl::PointCloud::Ptr spherical_centers( + new pcl::PointCloud()); + int top_pt = 0; + int index = 0; // Auxiliar index to be used inside loop + for (pcl::PointCloud::iterator pt = pc->points.begin(); + pt < pc->points.end(); pt++, index++) { + pcl::PointXYZ spherical_center; + spherical_center.x = atan2(pt->y, pt->x); // Horizontal + spherical_center.y = + atan2(sqrt(pt->x * pt->x + pt->y * pt->y), pt->z); // Vertical + spherical_center.z = + sqrt(pt->x * pt->x + pt->y * pt->y + pt->z * pt->z); // Range + spherical_centers->push_back(spherical_center); + + if (spherical_center.y < spherical_centers->points[top_pt].y) { + top_pt = index; } } -} -void sortPatternCentersYZ(pcl::PointCloud::Ptr pc, vector &v){ - double avg_y = 0, avg_z = 0; - for(pcl::PointCloud::iterator it=pc->points.begin(); itpoints.end(); it++){ - avg_y += (*it).y; - avg_z += (*it).z; - } - - pcl::PointXYZ center; - center.y = avg_y/4.; - center.z = avg_z/4.; - - for(pcl::PointCloud::iterator it=pc->points.begin(); itpoints.end(); it++){ - double y_dif = (*it).y - center.y; - double z_dif = (*it).z - center.z; - - if(y_dif > 0 && z_dif > 0){ - v[0] = (*it); - }else if(y_dif < 0 && z_dif > 0){ - v[1] = (*it); - }else if(y_dif < 0 && z_dif < 0){ - v[2] = (*it); - }else{ - v[3] = (*it); + // Compute distances from top-most center to rest of points + vector distances; + for (int i = 0; i < 4; i++) { + pcl::PointXYZ pt = pc->points[i]; + pcl::PointXYZ upper_pt = pc->points[top_pt]; + distances.push_back(sqrt(pow(pt.x - upper_pt.x, 2) + + pow(pt.y - upper_pt.y, 2) + + pow(pt.z - upper_pt.z, 2))); + } + + // Get indices of closest and furthest points + int min_dist = (top_pt + 1) % 4, max_dist = top_pt; + for (int i = 0; i < 4; i++) { + if (i == top_pt) continue; + if (distances[i] > distances[max_dist]) { + max_dist = i; + } + if (distances[i] < distances[min_dist]) { + min_dist = i; } } -} -void colourCenters(const pcl::PointCloud::Ptr pc, pcl::PointCloud::Ptr coloured){ - double avg_x = 0, avg_y = 0; - for(pcl::PointCloud::iterator it=pc->points.begin(); itpoints.end(); it++){ - avg_x += (*it).x; - avg_y += (*it).y; + // Second highest point shoud be the one whose distance is the median value + int top_pt2 = 6 - (top_pt + max_dist + min_dist); // 0 + 1 + 2 + 3 = 6 + + // Order upper row centers + int lefttop_pt = top_pt; + int righttop_pt = top_pt2; + + if (spherical_centers->points[top_pt].x < + spherical_centers->points[top_pt2].x) { + int aux = lefttop_pt; + lefttop_pt = righttop_pt; + righttop_pt = aux; } - pcl::PointXYZ center; - center.x = avg_x/4.; - center.y = avg_y/4.; + // Swap indices if target is located in the pi,-pi discontinuity + double angle_diff = spherical_centers->points[lefttop_pt].x - + spherical_centers->points[righttop_pt].x; + if (angle_diff > M_PI - spherical_centers->points[lefttop_pt].x) { + int aux = lefttop_pt; + lefttop_pt = righttop_pt; + righttop_pt = aux; + } + + // Define bottom row centers using lefttop == top_pt as hypothesis + int leftbottom_pt = min_dist; + int rightbottom_pt = max_dist; - for(pcl::PointCloud::iterator it=pc->points.begin(); itpoints.end(); it++){ - double x_dif = (*it).x - center.x; - double y_dif = (*it).y - center.y; + // If lefttop != top_pt, swap indices + if (righttop_pt == top_pt) { + leftbottom_pt = max_dist; + rightbottom_pt = min_dist; + } + // Fill vector with sorted centers + v[0] = pc->points[lefttop_pt]; // lt + v[1] = pc->points[righttop_pt]; // rt + v[2] = pc->points[rightbottom_pt]; // rb + v[3] = pc->points[leftbottom_pt]; // lb +} +void colourCenters(const std::vector pc, + pcl::PointCloud::Ptr coloured) { + float intensity = 0; + for (int i = 0; i < 4; i++) { pcl::PointXYZI cc; - cc.x = (*it).x; - cc.y = (*it).y; - cc.z = (*it).z; - - if(x_dif < 0 && y_dif < 0){ - cc.intensity = 0; - }else if(x_dif > 0 && y_dif < 0){ - cc.intensity = 0.3; - }else if(x_dif > 0 && y_dif > 0){ - cc.intensity = 0.6; - }else{ - cc.intensity = 1; - } + cc.x = pc[i].x; + cc.y = pc[i].y; + cc.z = pc[i].z; + + cc.intensity = intensity; coloured->push_back(cc); + intensity += 0.3; } } -void getCenterClusters(pcl::PointCloud::Ptr cloud_in, pcl::PointCloud::Ptr centers_cloud, - double cluster_tolerance = 0.10, int min_cluster_size = 15, int max_cluster_size = 200, bool verbosity=true){ - - pcl::search::KdTree::Ptr tree (new pcl::search::KdTree); - tree->setInputCloud (cloud_in); +void getCenterClusters(pcl::PointCloud::Ptr cloud_in, + pcl::PointCloud::Ptr centers_cloud, + double cluster_tolerance = 0.10, + int min_cluster_size = 15, int max_cluster_size = 200, + bool verbosity = true) { + pcl::search::KdTree::Ptr tree( + new pcl::search::KdTree); + tree->setInputCloud(cloud_in); std::vector cluster_indices; pcl::EuclideanClusterExtraction euclidean_cluster; - euclidean_cluster.setClusterTolerance (cluster_tolerance); - euclidean_cluster.setMinClusterSize (min_cluster_size); - euclidean_cluster.setMaxClusterSize (max_cluster_size); - euclidean_cluster.setSearchMethod (tree); - euclidean_cluster.setInputCloud (cloud_in); - euclidean_cluster.extract (cluster_indices); - - if(DEBUG && verbosity) cout << cluster_indices.size() << " clusters found from " << cloud_in->points.size() << " points in cloud" << endl; - - for (std::vector::iterator it=cluster_indices.begin(); itpoints.size() << " points in cloud" << endl; + + for (std::vector::iterator it = cluster_indices.begin(); + it < cluster_indices.end(); it++) { float accx = 0., accy = 0., accz = 0.; - for(vector::iterator it2=it->indices.begin(); it2indices.end(); it2++){ - accx+=cloud_in->at(*it2).x; - accy+=cloud_in->at(*it2).y; - accz+=cloud_in->at(*it2).z; + for (vector::iterator it2 = it->indices.begin(); + it2 < it->indices.end(); it2++) { + accx += cloud_in->at(*it2).x; + accy += cloud_in->at(*it2).y; + accz += cloud_in->at(*it2).z; } // Compute and add center to clouds pcl::PointXYZ center; - center.x = accx/it->indices.size(); - center.y = accy/it->indices.size(); - center.z = accz/it->indices.size(); + center.x = accx / it->indices.size(); + center.y = accy / it->indices.size(); + center.z = accz / it->indices.size(); centers_cloud->push_back(center); } } -Eigen::Affine3f getRotationMatrix(Eigen::Vector3f source, Eigen::Vector3f target){ +Eigen::Affine3f getRotationMatrix(Eigen::Vector3f source, + Eigen::Vector3f target) { Eigen::Vector3f rotation_vector = target.cross(source); rotation_vector.normalize(); - double theta = acos(source[2]/sqrt( pow(source[0],2)+ pow(source[1],2) + pow(source[2],2))); + double theta = acos(source[2] / sqrt(pow(source[0], 2) + pow(source[1], 2) + + pow(source[2], 2))); - if(DEBUG) ROS_INFO("Rot. vector: (%lf %lf %lf) / Angle: %lf", rotation_vector[0], rotation_vector[1], rotation_vector[2], theta); + if (DEBUG) + cout << "Rot. vector: " << rotation_vector << " / Angle: " << theta << endl; - Eigen::Matrix3f rotation = Eigen::AngleAxis(theta, rotation_vector) * Eigen::Scaling(1.0f); + Eigen::Matrix3f rotation = + Eigen::AngleAxis(theta, rotation_vector) * Eigen::Scaling(1.0f); Eigen::Affine3f rot(rotation); return rot; } +class Square { + private: + pcl::PointXYZ _center; + std::vector _candidates; + float _target_width, _target_height, _target_diagonal; + + public: + Square(std::vector candidates, float width, float height) { + _candidates = candidates; + _target_width = width; + _target_height = height; + _target_diagonal = sqrt(pow(width, 2) + pow(height, 2)); + + // Compute candidates centroid + for (int i = 0; i < candidates.size(); ++i) { + _center.x += candidates[i].x; + _center.y += candidates[i].y; + _center.z += candidates[i].z; + } + + _center.x /= candidates.size(); + _center.y /= candidates.size(); + _center.z /= candidates.size(); + } + + float distance(pcl::PointXYZ pt1, pcl::PointXYZ pt2) { + return sqrt(pow(pt1.x - pt2.x, 2) + pow(pt1.y - pt2.y, 2) + + pow(pt1.z - pt2.z, 2)); + } + + float perimeter() { // TODO: It is assumed that _candidates are ordered, it + // shouldn't + float perimeter = 0; + for (int i = 0; i < 4; ++i) { + perimeter += distance(_candidates[i], _candidates[(i + 1) % 4]); + } + return perimeter; + } + + pcl::PointXYZ at(int i) { + assert(0 <= i && i < 4); + return _candidates[i]; + } + + bool is_valid() { + pcl::PointCloud::Ptr candidates_cloud( + new pcl::PointCloud()); + // Check if candidates are at 5% of target's diagonal/2 to their centroid + for (int i = 0; i < _candidates.size(); ++i) { + candidates_cloud->push_back(_candidates[i]); + float d = distance(_center, _candidates[i]); + if (fabs(d - _target_diagonal / 2.) / (_target_diagonal / 2.) > + GEOMETRY_TOLERANCE) { + return false; + } + } + // Check perimeter? + std::vector sorted_centers; + sortPatternCenters(candidates_cloud, sorted_centers); + float perimeter = 0; + for (int i = 0; i < sorted_centers.size(); ++i) { + float current_distance = distance( + sorted_centers[i], sorted_centers[(i + 1) % sorted_centers.size()]); + if (i % 2) { + if (fabs(current_distance - _target_height) / _target_height > + GEOMETRY_TOLERANCE) { + return false; + } + } else { + if (fabs(current_distance - _target_width) / _target_width > + GEOMETRY_TOLERANCE) { + return false; + } + } + perimeter += current_distance; + } + float ideal_perimeter = (2 * _target_width + 2 * _target_height); + if (fabs((perimeter - ideal_perimeter) / ideal_perimeter > + GEOMETRY_TOLERANCE)) { + return false; + } + + // Check width + height? + return true; + } +}; + +void comb(int N, int K, std::vector> &groups) { + int upper_factorial = 1; + int lower_factorial = 1; + + for (int i = 0; i < K; i++) { + upper_factorial *= (N - i); + lower_factorial *= (K - i); + } + int n_permutations = upper_factorial / lower_factorial; + + if (DEBUG) + cout << N << " centers found. Iterating over " << n_permutations + << " possible sets of candidates" << endl; + + std::string bitmask(K, 1); // K leading 1's + bitmask.resize(N, 0); // N-K trailing 0's + + // print integers and permute bitmask + do { + std::vector group; + for (int i = 0; i < N; ++i) // [0..N-1] integers + { + if (bitmask[i]) { + group.push_back(i); + } + } + groups.push_back(group); + } while (std::prev_permutation(bitmask.begin(), bitmask.end())); + + assert(groups.size() == n_permutations); +} + +const std::string currentDateTime() { + time_t now = time(0); + struct tm tstruct; + char buf[80]; + tstruct = *localtime(&now); + strftime(buf, sizeof(buf), "%Y-%m-%d.%X", &tstruct); + + return buf; +} + #endif diff --git a/launch/fake_tf.launch b/launch/fake_tf.launch deleted file mode 100644 index 6d868bb..0000000 --- a/launch/fake_tf.launch +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - diff --git a/launch/full_calibration.launch b/launch/full_calibration.launch deleted file mode 100644 index 56eb7db..0000000 --- a/launch/full_calibration.launch +++ /dev/null @@ -1,5 +0,0 @@ - - - - - diff --git a/launch/laser_pattern.launch b/launch/laser_pattern.launch deleted file mode 100644 index c17740e..0000000 --- a/launch/laser_pattern.launch +++ /dev/null @@ -1,47 +0,0 @@ - - - - - - - - - - - - - filter_field_name: y - filter_limit_min: -100 - filter_limit_max: 100 - filter_limit_negative: False - max_queue_size: 1 - keep_organized: False - - - - - - - - filter_field_name: x - filter_limit_min: 0 - filter_limit_max: 100 - filter_limit_negative: False - max_queue_size: 1 - keep_organized: False - - - - - - x: 0 - y: 0 - z: 1 - angle_threshold: 0.55 - circle_radius: 0.12 - passthrough_radius_min: 1.0 - passthrough_radius_max: 6.0 - - - - diff --git a/launch/levinson.launch b/launch/levinson.launch deleted file mode 100644 index d182bbf..0000000 --- a/launch/levinson.launch +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - - - - - - - diff --git a/launch/levinson_edges.launch b/launch/levinson_edges.launch deleted file mode 100644 index 8784be8..0000000 --- a/launch/levinson_edges.launch +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/launch/lidar_pattern.launch b/launch/lidar_pattern.launch new file mode 100644 index 0000000..0906230 --- /dev/null +++ b/launch/lidar_pattern.launch @@ -0,0 +1,53 @@ + + + + + + + + + + + + + filter_field_name: z + filter_limit_min: -2 + filter_limit_max: 1 + filter_limit_negative: False + max_queue_size: 1 + + + + + + + + filter_field_name: y + filter_limit_min: -100 + filter_limit_max: 100 + filter_limit_negative: False + max_queue_size: 1 + + + + + + + + filter_field_name: x + filter_limit_min: 0 + filter_limit_max: 100 + filter_limit_negative: False + max_queue_size: 1 + + + + + + + passthrough_radius_min: 1.0 + passthrough_radius_max: 6.0 + + + + diff --git a/launch/mono_pattern.launch b/launch/mono_pattern.launch new file mode 100644 index 0000000..1db2958 --- /dev/null +++ b/launch/mono_pattern.launch @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + + + diff --git a/launch/pcl_coloring.launch b/launch/pcl_coloring.launch deleted file mode 100644 index b277e92..0000000 --- a/launch/pcl_coloring.launch +++ /dev/null @@ -1,9 +0,0 @@ - - - - - - - - - diff --git a/launch/pcl_projection.launch b/launch/pcl_projection.launch deleted file mode 100644 index 78757fe..0000000 --- a/launch/pcl_projection.launch +++ /dev/null @@ -1,9 +0,0 @@ - - - - - - - - - diff --git a/launch/real_laser_pattern.launch b/launch/real_laser_pattern.launch deleted file mode 100644 index af34157..0000000 --- a/launch/real_laser_pattern.launch +++ /dev/null @@ -1,47 +0,0 @@ - - - - - - - - - - - - - filter_field_name: y - filter_limit_min: -1.5 - filter_limit_max: 1.5 - filter_limit_negative: False - max_queue_size: 1 - keep_organized: False - - - - - - - - filter_field_name: x - filter_limit_min: 1 - filter_limit_max: 15 - filter_limit_negative: False - max_queue_size: 1 - keep_organized: False - - - - - - x: 0 - y: 0 - z: 1 - angle_threshold: 0.55 - circle_radius: 0.12 - passthrough_radius_min: 1.5 - passthrough_radius_max: 5.5 - - - - diff --git a/launch/real_stereo_pattern.launch b/launch/real_stereo_pattern.launch deleted file mode 100644 index 81a4627..0000000 --- a/launch/real_stereo_pattern.launch +++ /dev/null @@ -1,138 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - filter_field_name: z - filter_limit_min: 2 - filter_limit_max: 4 - filter_limit_negative: False - max_queue_size: 1 - keep_organized: False - - - - - - - - - filter_field_name: z - filter_limit_min: 2 - filter_limit_max: 4 - filter_limit_negative: False - max_queue_size: 1 - keep_organized: False - - - - - - - - segmentation_type: 1 - axis: [0.0, 1.0, 0.0] - threshold: 0.01 - eps_angle: 0.6 - - - - - - - - - negative: false - - - - - - - - - - - - - - - - - - - - - - negative: false - - - - - diff --git a/launch/registration.launch b/launch/registration.launch new file mode 100644 index 0000000..ab06f4a --- /dev/null +++ b/launch/registration.launch @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + diff --git a/launch/stereo_pattern.launch b/launch/stereo_pattern.launch index ce1a3c2..a71ff2d 100644 --- a/launch/stereo_pattern.launch +++ b/launch/stereo_pattern.launch @@ -1,57 +1,67 @@ + + + + - + - + - - - - - - - - - - - - + args="load stereo_image_proc/disparity stereo_manager_$(arg sensor_id)"> + + disparity_range: 128 + correlation_window_size: 5 + stereo_algorithm: 1 + prefilter_cap: 63 + uniqueness_ratio: 10.0 + speckle_size: 100 + speckle_range: 2 + P1: 216 + P2: 864 + disp12MaxDiff: 1 + + + + args="load stereo_image_proc/point_cloud2 stereo_manager_$(arg sensor_id) _queue_size:=100"> - - - - - - - - - - - - + + + use_camera_info: False + debug_view: False + edge_type: 0 + canny_threshold1: 100 + canny_threshold2: 200 + apertureSize: 3 + apply_blur_pre: True + postBlurSize: 13 + postBlurSigma: 3.2 + apply_blur_post: False + L2gradient: False + - + - - + + + edges_threshold: 128 + - + - + - - - + + + filter_field_name: z filter_limit_min: 0.8 filter_limit_max: 3 - filter_limit_negative: False + filter_limit_negative: false max_queue_size: 1 - keep_organized: False + keep_organized: false - - - + + + filter_field_name: z filter_limit_min: 0.8 filter_limit_max: 3 - filter_limit_negative: False + filter_limit_negative: false max_queue_size: 1 - keep_organized: False + keep_organized: false - + - + segmentation_type: 1 axis: [0.0, 1.0, 0.0] - threshold: 0.01 + threshold: 0.1 eps_angle: 0.55 - - + + negative: false @@ -114,18 +124,13 @@ - - + + - - - - - - + - @@ -133,5 +138,5 @@ - + diff --git a/launch/velo2cam_calibration.launch b/launch/velo2cam_calibration.launch deleted file mode 100644 index 3e22b91..0000000 --- a/launch/velo2cam_calibration.launch +++ /dev/null @@ -1,10 +0,0 @@ - - - - - - - - - - diff --git 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