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

Robotics with GPU computing

Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Pytorch port of Google Research's LEAF Audio paper

an Evolutionary Algorithm assisted GAN

PyTorch implementation for the ICLR 2020 paper "Understanding the Limitations of Variational Mutual Information Estimators"

Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

Code for the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness"

[ICCV 2021] Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

A repo that contains all the mesh keys needed for mesh backend, along with a code example of how to use them in python

Scale-aware Automatic Augmentation for Object Detection (CVPR 2021)

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

SPTAG: A library for fast approximate nearest neighbor search

Binary Stochastic Neurons in PyTorch

Official code for "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes", CVPR2022

Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

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