This repository contains the Map Analysis package to evaluate the quality of mapping data using several intrinsic and comparison-based metrics. A version of this package was utilized to analyze competitor-submitted maps in real time during the DARPA Subterranean Challenge Final Event. The mapping data and metrics were presented throughout the SubT Challenge Finals livestream.
A publication on this work is in progress. For now, if you use this code in an academic context, please cite this repository.
IEEE Reference List Format:
A. Schang, J. Rogers, and A. Maio (2021) Map Analysis (Version 1.0) [Source code]. https://github.com/subtchallenge/map_analysis.
The Map Analysis package generates metrics for maps in the 3D point cloud format. Three of the metrics are intrinsic qualities of the map:
- Size -- total number of points in the point cloud map, which may be accumulated across multiple clouds when partial maps are provided
- Rate -- frequency of map updates (Hz)
- Type -- method of producing the map, especially when using multiple agents
- Multi -- each map message may be provided by different agents
- Partial -- each map message may provide only a portion of the total area mapped, i.e., submaps
- Merged -- each map message is cumulative and includes the most complete information available
The remaining map metrics are calculated based on comparison of the input map against a known "ground truth" point cloud produced by a) scanning real-world environments with surveying equipment, and b) uniformly sampling a known mesh for virtual environments.
- Inlier Cloud -- points in the input map within a (configurable) tolerance from the closest ground truth point
- Outlier Cloud -- points in the input map greater a (configurable) tolerance from the closest ground truth point
- Coverage -- percentage of ground truth points within a (configurable) distance of an input map point, i.e.,
<ground truth points within distance of an inlier point> / <total ground truth points> - Deviation -- percentage of input map points greater than a (configurable) distance from the nearest ground truth point, i.e.,
<outlier points> / <total submitted points>
The inlier and outlier clouds are published on the ROS topics MA_inlier_cloud and MA_outlier_cloud, and the remaining numerical metrics are published on the map_metrics topic.
While this package has only been tested using data from the SubT Challenge environments, this software is compatible with other environments provided a sufficiently accurate and dense ground truth pointcloud is known.
This package can also generate metrics based on another form of mapping -- reference points provided by object localization. In the SubT Challenge, these reference points were submitted as "artifact reports" identifying type and location of 10 specified object types. The reports are compared against surveyed "ground truth" locations to produce the following metrics:
- Root Mean Square Error -- standard deviation of the errors in reported artifact locations, where error is the Euclidean distance to the nearest artifact of the same type.
- Root Mean Square Error Limited to the XY Plane -- same as above, except the Euclidean distance is measured as projected onto the XY plane, i.e., error in the Z axis (height) is ignored.
These metrics are published on the ROS topic artifact_metrics.
This code has been tested using Ubuntu 18.04 and ROS Melodic. The following packages must also be installed:
sudo apt-get -y install ros-melodic-rosbag ros-melodic-dynamic-reconfigure libboost-program-options1.65.1 libpcl-common1.8 libpcl-octree1.8 libpcl-io1.8 libpcl-visualization1.8 libpcl-segmentation1.8
Then, set up and build this package in a catkin workspace.
mkdir -p mapping_analysis_ws/src
cd mapping_analysis_ws
catkin init
git clone [email protected]:subtchallenge/map_analysis.git src/map_analysis
catkin build
An example launch file is provided for real-time map analysis and can be launched with:
cd mapping_analysis_ws
source devel/setup.bash
roslaunch map_analysis map_analysis.launch
By default, this will listen for input maps on the /cloud and /clouds/* topics (the latter for partial maps or maps submitted by multiple agents) and will compare the maps against a test point cloud represented as the SubT Challenge logo.
You will see the white ground truth cloud points in RViz.
Now, let's provide a test input map to analyze by running:
cd src/map_analysis/test
python test_pcl2.py /cloud
Now you will see new points in RViz -- the green inlier cloud and red outlier cloud. Every time a map is processed, you can also see the metrics with rostopic echo /map_metrics.
If you look closely, you can also see example ground truth artifact locations as blue markers with object labels (backpack, survivor, etc.). You can submit an artifact report to be compared against these ground truth artifacts, for example:
rostopic pub /subt/artifact_reports map_analysis/ArtifactReport "timestamp: {secs: 1, nsecs: 0}
reported_artifact_type: 'rope'
reported_artifact_position: {x: 0.0, y: 9.5, z: 6.0}
closest_artifact_name: ''
distance: 0
points_scored: 0
total_score: 0"
Now there is a new line marker displaying the distance from the artifact report to the nearest ground truth artifact of the same type. The metrics associated with the artifact report can be viewed with rostopic echo /artifact_metrics.
Note: In the /subt/artifact_reports topic, only the reported_artifact_type and reported_artifact_position fields are used by the Map Analysis package. The message is formatted for compatibility with logs from the SubT Virtual Testbed.
The launch file at src/map_analysis/launch/map_analysis.launch allows you to easily change the following parameters:
env_pcd: The path to the ground truth point cloud file for a given environment (string, default: SubTLogo.pcd)env_gt: The file containing the ground truth artifacts in a given environment (string, default: artifacts.csv)rviz: Whether or not to launch RViz (bool, default: false)
Additional parameters can be adjusted during runtime using Dynamic Reconfiguration (i.e. rosrun rqt_reconfigure rqt_reconfigure)
inlier tolerance: The max range (in meters) a point can be from a ground truth point and be considered an inlier (float, default: 1.0)outlier tolerance: The range (in meters) at which a submitted point differs enough from ground truth to be considered an outlier (float, default: 1.0)accumulate_mode: Whether or not to accumulate successive pointclouds, use this when sending differential updates as opposed to the entire map every message (bool, default: true)min_leaf_size: The minimum leaf size to be applied for cloud downsampling prior to map analysis when necessary (float, default: 0.05)incremental_inlier_tolerance: Tolerance to use in accumulated point cloud for union operation (float, default: 0.1)
You can also remap from the default ROS topics within the launch file. For example, to listen for input maps on /map_cloud, you can add <remap from="cloud" to="/map_cloud"> within the map analysis node tag.
The map_analysis package has ability to process bagged pointcloud data in an offline mode to allow for an adequate amount of runtime to generate map metrics as quickly as possible without the possibility of skipping map updates. An example launch file is located under src/map_analysis/launch/bag_map_analysis.launch with the following additional parameters:
bag_in: Offline bag input file (string, default: input_map.bag)bag_out: Offline bag output file (string, default: output_map.bag)
The resulting processed bag will contain analysis topics in a compressed format with all of the map metrics and segmented pointclouds.
A quick method to view the resulting map metrics for the entire run from the bag is to run rostopic echo -b <bag_out> /map_metrics
The map_analysis package can also be run alongside an active bag playback to generate results with the sim_time parameter set to true.
This method should use the live analysis mode, not the offline bag analysis mode.
When analyzing mapping data from the SubT Challenge events, please utilize the following repositories to download ground truth point cloud (.pcd) files which are compatible with the Map Analysis package:
- Systems Competition - Tunnel Circuit
- Systems Competition - Urban Circuit
- Systems Competition - Final Event
- Virtual Competition - All Events
These can be cloned within your mapping_analysis_ws and loaded by using the path to a pcd file as your env_pcd argument during launch.
If you have any questions or comments about this repository, please contact:
- Arthur Schang - [email protected]
- John Rogers - [email protected]
- Angela Maio - [email protected]
- SubT Challenge Mailbox - [email protected]