From ed33a0ebf4df40e008b9bbc29020b2039f87fb83 Mon Sep 17 00:00:00 2001 From: Fabian Immel Date: Thu, 1 Feb 2024 19:39:56 +0100 Subject: [PATCH] add doc and readme --- README.md | 6 ++ lanelet2_ml_converter/README.md | 60 +++++++++++++++++- .../doc/summary_flowchart.png | Bin 0 -> 158452 bytes .../test/test_map_data_interface.cpp | 4 +- 4 files changed, 66 insertions(+), 4 deletions(-) create mode 100644 lanelet2_ml_converter/doc/summary_flowchart.png diff --git a/README.md b/README.md index a7049829..4229f3db 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,10 @@ https://build.ros2.org/job/Hdev__lanelet2__ubuntu_jammy_amd64/lastBuild/) [![PyPI Downloads](https://img.shields.io/pypi/dm/lanelet2.svg?label=PyPI%20downloads)]( https://pypi.org/project/lanelet2/) +## New module :rocket: : `lanelet2_ml_converter` + +Convert Lanelet2 maps directly to instance labels for map perception and other map-based learning tasks! `lanelet2_ml_converter` provides local instance labels of various abstraction layers and representations accessible as numpy arrays from python. Check out the `lanelet2_ml_converter` module for more information and usage examples. + ## Overview Lanelet2 is a C++ library for handling map data in the context of automated driving. It is designed to utilize high-definition map data in order to efficiently handle the challenges posed to a vehicle in complex traffic scenarios. Flexibility and extensibility are some of the core principles to handle the upcoming challenges of future maps. @@ -30,6 +34,7 @@ Features: - **Boost Geometry** support for all thinkable kinds of geometry calculations on map primitives - Released under the [**BSD 3-Clause license**](LICENSE) - Support for **ROS1, ROS2, Docker and Conan** (see instructions below) +- Converter module to convert Lanelet2 maps into **local instance labels for machine learning tasks**. ![](lanelet2_core/doc/images/lanelet2_example_image.png) @@ -181,6 +186,7 @@ Examples and common use cases in both C++ and Python can be found [here](lanelet * **lanelet2_routing** implements the routing graph for routing or reachable set or queries as well as collision checking * **lanelet2_maps** provides example maps and functionality to visualize and modify them easily in JOSM * **lanelet2_matching** provides functions to determine in which lanelet an object is/could be currently located +* **lanelet2_ml_converter** converts Lanelet2 maps into local instance labels for machine learning tasks * **lanelet2_python** implements the python interface for lanelet2 * **lanelet2_validation** provides checks to ensure a valid lanelet2 map * **lanelet2_examples** contains tutorials for working with Lanelet2 in C++ and Python diff --git a/lanelet2_ml_converter/README.md b/lanelet2_ml_converter/README.md index 7a956019..f78ca7e5 100644 --- a/lanelet2_ml_converter/README.md +++ b/lanelet2_ml_converter/README.md @@ -1,3 +1,61 @@ # Lanelet2 ML Converter -Converter module to convert Lanelet2 maps into local label instances for machine learning tasks. \ No newline at end of file +Converter module to convert Lanelet2 maps into local instance labels for machine learning tasks. + +![](doc/summary_flowchart.png) + +## Usage Examples + +### Python + +```python +import lanelet2 +from lanelet2.core import BasicPoint2d +from lanelet2.ml_converter import MapDataInterface + +pos = BasicPoint2d(10, 10) # set your local reference frame origin +yaw = 0 # set your local reference frame yaw angle (heading) +mDataIf = MapDataInterface(ll2_map) # get the MapDataInterface object and pass the ll2 map +mDataIf.setCurrPosAndExtractSubmap(pos, yaw) # extract the local submap +lData = mDataIf.laneData(True) # get the LaneData local instance labels +tfData = lData.getTensorFeatureData(True, False) # get the local instance labels as numpy arrays +``` + +### C++ +```c++ +#include "lanelet2_ml_converter/MapData.h" +#include "lanelet2_ml_converter/MapDataInterface.h" +#include "lanelet2_ml_converter/MapFeatures.h" + +using TensorData = lanelet::ml_converter::LaneData::TensorFeatureData; + +lanelet::BasicPoint2d pos(10, 10); // set your local reference frame origin +double yaw = 0; // set your local reference frame yaw angle (heading) +MapDataInterface mDataIf(laneletMap); // get the MapDataInterface object and pass the ll2 map +mDataIf.setCurrPosAndExtractSubmap(pos, yaw); // extract the local submap +lanelet::ml_converter::LaneData lData = mDataIf.laneData(True); // get the LaneData local instance labels +TensorData tData = lData.getTensorFeatureData(True, False); // get the local instance labels as Eigen mats +``` + +## Components + +### `MapDataInterface` + +Main interface class that is used to generate `LaneData` objects. + +### `LaneData` + +`LaneData` objects hold all local instance labels of a local reference frame pose. + +### `TensorFeatureData` + +Subclass of `LaneData` that holds all local instance label in a convenient numpy array / Eigen matrix form. + +### `LaneLineStringFeature` / `CompoundLaneLineStringFeature` / `LaneletFeature` / ... + +Each object of these classes holds a single instance label. You can access various information such as the map element ID and the raw or partially processed LineStrings. + +## Important to know + +- **Road border tagging**: To correctly recognize road borders, they must be tagged as such! It is not possible to automatically recognize untagged road borders, they will be processed as lane dividers. +- **Python numpy arrays**: To make it possible to convert the underlying Eigen matrices to numpy arrays, they are copied for each access (such as calling `getTensorFeatureData`). 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a/lanelet2_ml_converter/test/test_map_data_interface.cpp b/lanelet2_ml_converter/test/test_map_data_interface.cpp index c7afbe9d..41ad323b 100644 --- a/lanelet2_ml_converter/test/test_map_data_interface.cpp +++ b/lanelet2_ml_converter/test/test_map_data_interface.cpp @@ -79,9 +79,7 @@ TEST_F(MLConverterTest, MapDataInterface) { // NOLINT // y.resize(mat.rows()); // Eigen::VectorXd::Map(&x[0], mat.rows()) = mat.col(0); // Eigen::VectorXd::Map(&y[0], mat.rows()) = mat.col(1); - // matplot::plot(x, y, "r")->line_width(3); - // } - // for (const auto& mat : compoundLaneDividers) { + // matplot::pltrafficRulesuto& mat : compoundLaneDividers) { // std::vector x; // std::vector y; // x.resize(mat.rows());