urban_road_filter: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles

Dependency

• ROS 2 (tested with and )
• PCL

Install (download and build)

Use the following commands to download and compile the package.

cd ~/ros2_ws/src

git clone https://github.com/jkk-research/urban_road_filter -b ros2

And build:

cd ~/ros2_ws

colcon build --packages-select urban_road_filter --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release

Getting started

Issue the following commands to start ROS 2, play sample data, and start the algorithm with visualization.

In a new terminal start ROS 2:

Don't forget to source your workspace first

Details

source ~/ros2_ws/install/setup.bash

ros2 launch urban_road_filter urban_road_filter.launch.py

Features

The urban_road_filter package provides the following features:

1. Detection Methods:

   • x_zero_method: Detects roadside points by analyzing the angle between three points while keeping the X-coordinate constant.
   • z_zero_method: Detects roadside points by analyzing the angle between two vectors while keeping the Z-coordinate constant.
   • star_shaped_method: Uses a star-shaped algorithm to detect roadside points within a sector.

2. Configurable Parameters:

   • Detection area dimensions (min_x, max_x, min_y, max_y, min_z, max_z).
   • LIDAR vertical resolution (interval).
   • Curb detection parameters (curb_height, curb_points).
   • Polygon simplification options (polysimp_allow, polysimp, polyz).

3. ROS Topics:

   • /road: Filtered points representing the drivable road.
   • /curb: Filtered points representing curbs.
   • /roi: Filtered points within the region of interest.
   • /road_marker: Visualization markers for detected roads.

4. Performance Metrics:

   • Publishes statistics about the segmentation results, including the number of points classified as road, curb, or non-road.

Configuration

The behavior of the urban_road_filter node can be customized using the parameters.yaml file. Key parameters include:

• fixed_frame: The fixed frame for the LIDAR data.
• topic_name: The topic to subscribe to for point cloud data.
• x_zero_method, z_zero_method, star_shaped_method: Enable or disable specific detection methods.
• interval: Acceptable interval for the LIDAR's vertical angular resolution.
• curb_height, curb_points: Parameters for curb detection.
• polysimp_allow, polysimp, polyz: Parameters for polygon simplification.

Cite & paper

If you use any of this code, please consider citing the paper:

@Article{roadfilt2022horv,
    title = {Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles},
    author = {Horváth, Ernő and Pozna, Claudiu and Unger, Miklós},
    journal = {Sensors},
    volume = {22},
    year = {2022},
    number = {1},
    url = {https://www.mdpi.com/1424-8220/22/1/194},
    issn = {1424-8220},
    doi = {10.3390/s22010194}
}

Related solutions

• points_preprocessor ray_ground_filter and ring_ground_filter (ROS)
• linefit_ground_segmentation (ROS)
• curb_detection (ROS)
• 3DLidar_curb_detection (ROS)
• lidar_filter
• Many more algorithms without code mentioned in the paper.

Videos and images

ROS publications / subscriptions

flowchart LR

P[points]:::gray -->|sensor_msgs/PointCloud2| U([urban_road_filt</br>node]):::gray
U --> |sensor_msgs/PointCloud2| A[curb]:::gray
U --> |sensor_msgs/PointCloud2| B[road]:::gray 
U --> |sensor_msgs/PointCloud2| C[road_probably]:::gray
U --> |sensor_msgs/PointCloud2| D[roi]:::gray
U --> |visualization_msgs/MarkerArray| E[road_marker]:::gray

classDef light fill:#34aec5,stroke:#152742,stroke-width:2px,color:#152742  
classDef dark fill:#152742,stroke:#34aec5,stroke-width:2px,color:#34aec5
classDef white fill:#ffffff,stroke:#152742,stroke-width:2px,color:#152742
classDef gray fill:#f6f8fa,stroke:#152742,stroke-width:2px,color:#152742
classDef red fill:#ef4638,stroke:#152742,stroke-width:2px,color:#fff

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