Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jkk-research/urban_road_filter
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗
https://github.com/jkk-research/urban_road_filter
autonomous-driving filter lidar lidar-filter point-cloud road-segmentation ros self-driving-car shell-eco-marathon sze szenergy
Last synced: about 1 month ago
JSON representation
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗
- Host: GitHub
- URL: https://github.com/jkk-research/urban_road_filter
- Owner: jkk-research
- License: bsd-3-clause
- Created: 2021-06-07T07:42:07.000Z (about 3 years ago)
- Default Branch: ros1
- Last Pushed: 2023-12-14T14:06:34.000Z (6 months ago)
- Last Synced: 2024-02-06T09:52:54.987Z (5 months ago)
- Topics: autonomous-driving, filter, lidar, lidar-filter, point-cloud, road-segmentation, ros, self-driving-car, shell-eco-marathon, sze, szenergy
- Language: C++
- Homepage:
- Size: 22.6 MB
- Stars: 261
- Watchers: 6
- Forks: 70
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- Awesome-Self-Driving - urban_road_filter - Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles. (6. Detection / 3.4. Others)
- awesome-robotic-tooling - urban_road_filter - Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles. (Sensor Processing / Lidar and Point Cloud Processing)
- awesome-lidar - GitHub repository :octocat:
- awesome-robotic-tooling - urban_road_filter - Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles. (Sensor Processing / Lidar and Point Cloud Processing)
- awesome-stars - jkk-research/urban_road_filter - Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗 (C++)
- Awesome-Autonomous-Driving - urban_road_filter
- awesome-mobile-robotics - Urban Road Filter - time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles (Softwares and Libraries)
README
# `urban_road_filter`: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles
# Dependency
- [ROS](http://wiki.ros.org/ROS/Installation) (tested with Kinetic and Melodic)
- [PCL](https://pointclouds.org/)
# Install
Use the following commands to download and compile the package.
```
cd ~/catkin_ws/src
git clone https://github.com/jkk-research/urban_road_filter
catkin build urban_road_filter
```
# Getting started
Issue the following commands to start roscore, download and play sample data, and start the algorithm with visualization. You can also watch this as a [youtube tutorial](https://www.youtube.com/watch?v=HHnj4VcbSy4).
In a **new terminal** start roscore:
```
roscore
```
In a **new terminal** go to your bag folder (e.g. `~/Downloads`):
```
cd ~/Downloads
```
Download a sample rosbag (~3,3 GB):
```r
wget https://laesze-my.sharepoint.com/:u:/g/personal/herno_o365_sze_hu/EYl_ahy5pgBBhNHt5ZkiBikBoy_j_x95E96rDtTsxueB_A?download=1 -O leaf-2021-04-23-campus.bag
```
Play rosbag:
```r
rosbag play -l ~/Downloads/leaf-2021-04-23-campus.bag
```
In a **new terminal** start the `urban_road_filter` node, `rviz` and `rqt_reconfigure` with roslaunch:
```
roslaunch urban_road_filter demo1.launch
```
# Cite & paper
If you use any of this code please consider citing the [paper](https://www.mdpi.com/1424-8220/22/1/194):
```bibtex
@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`](https://github.com/Autoware-AI/core_perception/tree/master/points_preprocessor) `ray_ground_filter` and `ring_ground_filter` (ROS)
- [`linefit_ground_segmentation`](https://github.com/lorenwel/linefit_ground_segmentation) (ROS)
- [`curb_detection`](https://github.com/linyliny/curb_detection) (ROS)
- [`3DLidar_curb_detection`](https://github.com/SohaibAl-emara/3D_Lidar_Curb_Detection) (ROS)
- [`lidar_filter`](https://github.com/ZoltanTozser/lidar_filter)
- Many more algorithms without code mentioned in the [paper](https://doi.org/10.3390/s22010194).
# Videos and images
[
](https://www.youtube.com/watch?v=T2qi4pldR-E)
[
](https://www.youtube.com/watch?v=HHnj4VcbSy4)
[
](https://www.youtube.com/watch?v=9tdzo2AyaHM)
[
](https://www.youtube.com/watch?v=lp6q_QvWA-Y)
# ROS publications / subscriptions
```mermaid
flowchart LR
P[points]:::gray -->|sensor_msgs/PointCloud2| U([urban_road_filtnode]):::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
```