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

GitHub Repository

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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

Pytorch implementation of Integrating Tree Path in Transformer for Code Representation

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Code for the paper Hybrid Spectrogram and Waveform Source Separation

We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

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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

Pytorch implementation of Integrating Tree Path in Transformer for Code Representation

Keras code and weights files for popular deep learning models.

This repository contains part of the code used to make the images visible in the article "How does an AI Imagine the Universe?" published on Towards Data Science.

[PNAS2021] The neural architecture of language: Integrative modeling converges on predictive processing

A small tool to joint picture including gif

An image classification app boilerplate to serve your deep learning models asap!

This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

Code for the paper Hybrid Spectrogram and Waveform Source Separation

We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

Fluency ENhanced Sentence-bert Evaluation (FENSE), metric for audio caption evaluation. And Benchmark dataset AudioCaps-Eval, Clotho-Eval.

Simple API for UCI Machine Learning Dataset Repository (search, download, analyze)

Reinforcement learning library in JAX.

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

The pure and clear PyTorch Distributed Training Framework.

[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

[ICCV 2021 Oral] NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo

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