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

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

Videos and images

Comments
  • If the given dataset have a preprocessing?

    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? img

    question 
    opened by LuYoKa 6
  • I need help

    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

    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

    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]

    2

    3

    opened by csaplaci 0
  • Semi-automated vector map building

    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)
Owner
JKK - Vehicle Industry Research Center
Széchenyi University's Research Center
JKK - Vehicle Industry Research Center
Cosine Annealing With Warmup

CosineAnnealingWithWarmup Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an

zhuyun 4 Apr 18, 2022
RLBot Python bindings for the Rust crate rl_ball_sym

RLBot Python bindings for rl_ball_sym 0.6 Prerequisites: Rust & Cargo Build Tools for Visual Studio RLBot - Verify that the file %localappdata%\RLBotG

Eric Veilleux 2 Nov 25, 2022
The Body Part Regression (BPR) model translates the anatomy in a radiologic volume into a machine-interpretable form.

Copyright © German Cancer Research Center (DKFZ), Division of Medical Image Computing (MIC). Please make sure that your usage of this code is in compl

MIC-DKFZ 40 Dec 18, 2022
Video-Music Transformer

VMT Video-Music Transformer (VMT) is an attention-based multi-modal model, which generates piano music for a given video. Paper https://arxiv.org/abs/

Chin-Tung Lin 5 Jul 13, 2022
Implementation for Homogeneous Unbalanced Regularized Optimal Transport

HUROT: An Homogeneous formulation of Unbalanced Regularized Optimal Transport. This repository provides code related to this preprint. This is an alph

Théo Lacombe 1 Feb 17, 2022
Bayesian inference for Permuton-induced Chinese Restaurant Process (NeurIPS2021).

Permuton-induced Chinese Restaurant Process Note: Currently only the Matlab version is available, but a Python version will be available soon! This is

NTT Communication Science Laboratories 3 Dec 17, 2022
Deep learning based hand gesture recognition using LSTM and MediaPipie.

Hand Gesture Recognition Deep learning based hand gesture recognition using LSTM and MediaPipie. Demo video using PingPong Robot Files Pretrained mode

Brad 24 Nov 11, 2022
Chinese license plate recognition

AgentCLPR 简介 一个基于 ONNXRuntime、AgentOCR 和 License-Plate-Detector 项目开发的中国车牌检测识别系统。 车牌识别效果 支持多种车牌的检测和识别(其中单层车牌识别效果较好): 单层车牌: [[[[373, 282], [69, 284],

AgentMaker 26 Dec 25, 2022
Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem

Benchmarking nearest neighbors Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem, but so far t

Erik Bernhardsson 3.2k Jan 03, 2023
[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception

Versatile Multi-Modal Pre-Training for Human-Centric Perception Fangzhou Hong1  Liang Pan1  Zhongang Cai1,2,3  Ziwei Liu1* 1S-Lab, Nanyang Technologic

Fangzhou Hong 96 Jan 03, 2023
Density-aware Single Image De-raining using a Multi-stream Dense Network (CVPR 2018)

DID-MDN Density-aware Single Image De-raining using a Multi-stream Dense Network He Zhang, Vishal M. Patel [Paper Link] (CVPR'18) We present a novel d

He Zhang 224 Dec 12, 2022
UV matrix decompostion using movielens dataset

UV-matrix-decompostion-with-kfold UV matrix decompostion using movielens dataset upload the 'ratings.dat' file install the following python libraries

2 Oct 18, 2022
CL-Gym: Full-Featured PyTorch Library for Continual Learning

CL-Gym: Full-Featured PyTorch Library for Continual Learning CL-Gym is a small yet very flexible library for continual learning research and developme

Iman Mirzadeh 36 Dec 25, 2022
QilingLab challenge writeup

qiling lab writeup shielder 在 2021/7/21 發布了 QilingLab 來幫助學習 qiling framwork 的用法,剛好最近有用到,順手解了一下並寫了一下 writeup。 前情提要 Qiling 是一款功能強大的模擬框架,和 qemu user mode

Yuan 17 Nov 17, 2022
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
Deep Learning for Computer Vision final project

Deep Learning for Computer Vision final project

grassking100 1 Nov 30, 2021
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation This is the implementation of RATE: Overcoming Noise and Spar

Yu Zhang 5 Feb 10, 2022
RefineGNN - Iterative refinement graph neural network for antibody sequence-structure co-design (RefineGNN)

Iterative refinement graph neural network for antibody sequence-structure co-des

Wengong Jin 83 Dec 31, 2022
Denoising images with Fourier Ring Correlation loss

Denoising images with Fourier Ring Correlation loss The python code accompanies the working manuscript Image quality measurements and denoising using

2 Mar 12, 2022
Look Who’s Talking: Active Speaker Detection in the Wild

Look Who's Talking: Active Speaker Detection in the Wild Dependencies pip install -r requirements.txt In addition to the Python dependencies, ffmpeg

Clova AI Research 60 Dec 08, 2022