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

Related tags

Deep Learningfilterpoint-cloudroslidarself-driving-carautonomous-drivingroad-segmentationlidar-filter
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
GitHub Repository
Cross-modal Deep Face Normals with Deactivable Skip Connections

Cross-modal Deep Face Normals with Deactivable Skip Connections Victoria Fernández Abrevaya*, Adnane Boukhayma*, Philip H. S. Torr, Edmond Boyer (*Equ

72 Nov 27, 2022
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation.

Understanding Minimum Bayes Risk Decoding This repo provides code and documentation for the following paper: Müller and Sennrich (2021): Understanding

ZurichNLP 13 May 01, 2022
Knowledge Distillation Toolbox for Semantic Segmentation

SegDistill: Toolbox for Knowledge Distillation on Semantic Segmentation Networks This repo contains the supported code and configuration files for Seg

9 Dec 12, 2022
Adversarial-Information-Bottleneck - Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)

NeurIPS 2021 Title: Distilling Robust and Non-Robust Features in Adversarial Exa

LBK 35 Dec 26, 2022
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval (NeurIPS'21)

Baleen Baleen is a state-of-the-art model for multi-hop reasoning, enabling scalable multi-hop search over massive collections for knowledge-intensive

Stanford Future Data Systems 22 Dec 05, 2022
PyTorch implementation for our paper Learning Character-Agnostic Motion for Motion Retargeting in 2D, SIGGRAPH 2019

Learning Character-Agnostic Motion for Motion Retargeting in 2D We provide PyTorch implementation for our paper Learning Character-Agnostic Motion for

Rundi Wu 367 Dec 22, 2022
This repository contains the scripts for downloading and validating scripts for the documents

HC4: HLTCOE CLIR Common-Crawl Collection This repository contains the scripts for downloading and validating scripts for the documents. Document ids,

JHU Human Language Technology Center of Excellence 6 Jun 07, 2022
Vehicles Counting using YOLOv4 + DeepSORT + Flask + Ngrok

A project for counting vehicles using YOLOv4 + DeepSORT + Flask + Ngrok

Duong Tran Thanh 37 Dec 16, 2022
Explore extreme compression for pre-trained language models

Code for paper "Exploring extreme parameter compression for pre-trained language models ICLR2022"

twinkle 16 Nov 14, 2022
Continuous Augmented Positional Embeddings (CAPE) implementation for PyTorch

PyTorch implementation of Continuous Augmented Positional Embeddings (CAPE), by Likhomanenko et al. Enhance your Transformer positional embeddings with easy-to-use augmentations!

Guillermo Cámbara 26 Dec 13, 2022
Official code of paper "PGT: A Progressive Method for Training Models on Long Videos" on CVPR2021

PGT Code for paper PGT: A Progressive Method for Training Models on Long Videos. Install Run pip install -r requirements.txt. Run python setup.py buil

Bo Pang 27 Mar 30, 2022
SmartSim Infrastructure Library.

Home Install Documentation Slack Invite Cray Labs SmartSim SmartSim makes it easier to use common Machine Learning (ML) libraries like PyTorch and Ten

Cray Labs 139 Jan 01, 2023
Memory-efficient optimum einsum using opt_einsum planning and PyTorch kernels.

opt-einsum-torch There have been many implementations of Einstein's summation. numpy's numpy.einsum is the least efficient one as it only runs in sing

Haoyan Huo 9 Nov 18, 2022
PyTorch implementation of GLOM

GLOM PyTorch implementation of GLOM, Geoffrey Hinton's new idea that integrates concepts from neural fields, top-down-bottom-up processing, and attent

Yeonwoo Sung 20 Aug 17, 2022
[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Robot Action Primitives (RAPS) This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action

Murtaza Dalal 55 Dec 27, 2022
Summary of related papers on visual attention

This repo is built for paper: Attention Mechanisms in Computer Vision: A Survey paper Vision-Attention-Papers Channel attention Spatial attention Temp

MenghaoGuo 2.1k Dec 30, 2022
PyTorch version implementation of DORN

DORN_PyTorch This is a PyTorch version implementation of DORN Reference H. Fu, M. Gong, C. Wang, K. Batmanghelich and D. Tao: Deep Ordinal Regression

Zilin.Zhang 3 Apr 27, 2022
"Neural Turing Machine" in Tensorflow

Neural Turing Machine in Tensorflow Tensorflow implementation of Neural Turing Machine. This implementation uses an LSTM controller. NTM models with m

Taehoon Kim 1k Dec 06, 2022
A light and fast one class detection framework for edge devices. We provide face detector, head detector, pedestrian detector, vehicle detector......

A Light and Fast Face Detector for Edge Devices Big News: LFD, which is a big update of LFFD, now is released (2021.03.09). It is strongly recommended

YonghaoHe 1.3k Dec 25, 2022
Multi Camera Calibration

Multi Camera Calibration 'modules/camera_calibration/app/camera_calibration.cpp' is for calculating extrinsic parameter of each individual cameras. 'm

7 Dec 01, 2022