Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

Last update: Dec 12, 2022

Overview

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

Dependency

ROS (tested with Kinetic and Melodic)
PCL

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

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}
}

Realated solutions

points_preprocessor ray_ground_filter and ring_ground_filter

Videos and images

Comments

If the given dataset have a preprocessing?

Thanks for your great work! I try to do some experiment on kitti dataset. But I found it does not have the same effect as yours. The blue marks, as shown in the following image, are false positive. I want to wonder if the given dataset have a preprocessing?
question

opened by LuYoKa 6
I need help

Hello, I follow the steps to generate this error. How should I solve it? Thanks Please submit a full bug report, with preprocessed source if appropriate. See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions. urban_road_filter/CMakeFiles/lidar_road.dir/build.make:75: recipe for target 'urban_road_filter/CMakeFiles/lidar_road.dir/src/lidar_segmentation.cpp.o' failed make[2]: *** [urban_road_filter/CMakeFiles/lidar_road.dir/src/lidar_segmentation.cpp.o] Error 4 make[2]: *** 正在等待未完成的任务.... c++: internal compiler error: 已杀死 (program cc1plus) Please submit a full bug report, with preprocessed source if appropriate. See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions. urban_road_filter/CMakeFiles/lidar_road.dir/build.make:131: recipe for target 'urban_road_filter/CMakeFiles/lidar_road.dir/src/z_zero_method.cpp.o' failed make[2]: *** [urban_road_filter/CMakeFiles/lidar_road.dir/src/z_zero_method.cpp.o] Error 4 c++: internal compiler error: 已杀死 (program cc1plus) Please submit a full bug report, with preprocessed source if appropriate. See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions. urban_road_filter/CMakeFiles/lidar_road.dir/build.make:89: recipe for target 'urban_road_filter/CMakeFiles/lidar_road.dir/src/main.cpp.o' failed make[2]: *** [urban_road_filter/CMakeFiles/lidar_road.dir/src/main.cpp.o] Error 4 CMakeFiles/Makefile2:2521: recipe for target 'urban_road_filter/CMakeFiles/lidar_road.dir/all' failed make[1]: *** [urban_road_filter/CMakeFiles/lidar_road.dir/all] Error 2 Makefile:145: recipe for target 'all' failed make: *** [all] Error 2 Invoking "make -j8 -l8" failed
question

opened by chaohe1998 2
Follow ROS naming conventions
Naming ROS resources: http://wiki.ros.org/ROS/Patterns/Conventions

Package naming: https://www.ros.org/reps/rep-0144.html

Naming conventions for drivers: https://ros.org/reps/rep-0135.html

Parameter namespacing: http://wiki.ros.org/Parameter%20Server

e.g. visualization_MarkerArray is not a valid topic name
enhancement
opened by horverno 1
StarShapedSearch algorithm not functioning properly

The "star shaped search" detection algorithm seems to function with reduced range and [by angle] only in the first quarter of its detection area (counter-clockwise / positive z angles from x-axis, right-handed coordinate-system).

The images below show the output using only this algorithm (other detection methods, blind spot correction and output polygon simplification turned off).

[red line = polygon connecting the detected points]

opened by csaplaci 0
Semi-automated vector map building

New feature:

Based on the urban_road_filter output a semi-automated vector map building (e.g. lanelet2 / opendrive) in the global frame (e.g. map)

(small help)
enhancement feature

opened by horverno 1

Releases(paper)

paper(Mar 10, 2022)

The verison described in the paper
Source code(tar.gz)
Source code(zip)

Owner

JKK - Vehicle Industry Research Center

Széchenyi University's Research Center

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Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

Related tags

Overview

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

Dependency

Install

Getting started

Cite & paper

Realated solutions

Videos and images

Comments

If the given dataset have a preprocessing?

I need help

Follow ROS naming conventions

StarShapedSearch algorithm not functioning properly

Semi-automated vector map building

Releases(paper)

paper(Mar 10, 2022)

Owner

JKK - Vehicle Industry Research Center

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Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

Related tags

Overview

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

Dependency

Install

Getting started

Cite & paper

Realated solutions

Videos and images

Comments

If the given dataset have a preprocessing?

I need help

Follow ROS naming conventions

StarShapedSearch algorithm not functioning properly

Semi-automated vector map building

Releases(paper)

paper(Mar 10, 2022)

Owner

JKK - Vehicle Industry Research Center

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

Deep Learning Head Pose Estimation using PyTorch.

This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

✨风纪委员会自动投票脚本，利用Github Action帮你进行裁决操作（为了让其他风纪委员有案件可判，本程序从中午12点才开始运行，有需要请自己修改运行时间）

The 2nd place solution of 2021 google landmark retrieval on kaggle.

Doing the asl sign language classification on static images using graph neural networks.

Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

An open framework for Federated Learning.

TensorFlow 2 AI/ML library wrapper for openFrameworks

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