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
PyTorch implementation of EGVSR: Efficcient & Generic Video Super-Resolution (VSR)

This is a PyTorch implementation of EGVSR: Efficcient & Generic Video Super-Resolution (VSR), using subpixel convolution to optimize the inference speed of TecoGAN VSR model. Please refer to the offi

789 Jan 04, 2023
classify fashion-mnist dataset with pytorch

Fashion-Mnist Classifier with PyTorch Inference 1- clone this repository: git clone https://github.com/Jhamed7/Fashion-Mnist-Classifier.git 2- Instal

1 Jan 14, 2022
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering Paper: https://arxiv.org/abs/2103.00762 Running Run on the provided DTU scene cd run ba

Fanbo Xiang 67 Dec 28, 2022
E-RAFT: Dense Optical Flow from Event Cameras

E-RAFT: Dense Optical Flow from Event Cameras This is the code for the paper E-RAFT: Dense Optical Flow from Event Cameras by Mathias Gehrig, Mario Mi

Robotics and Perception Group 71 Dec 12, 2022
Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

NodePiece - Compositional and Parameter-Efficient Representations for Large Knowledge Graphs NodePiece is a "tokenizer" for reducing entity vocabulary

Michael Galkin 107 Jan 04, 2023
PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Anomaly Transformer in PyTorch This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This pape

spencerbraun 160 Dec 19, 2022
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

Nervana 3.9k Dec 20, 2022
ByteTrack: Multi-Object Tracking by Associating Every Detection Box

ByteTrack ByteTrack is a simple, fast and strong multi-object tracker. ByteTrack: Multi-Object Tracking by Associating Every Detection Box Yifu Zhang,

Yifu Zhang 2.9k Jan 04, 2023
This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

3D-CVF This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object

YecheolKim 97 Dec 20, 2022
Pytorch implementation of the paper Time-series Generative Adversarial Networks

TimeGAN-pytorch Pytorch implementation of the paper Time-series Generative Adversarial Networks presented at NeurIPS'19. Jinsung Yoon, Daniel Jarrett

Zhiwei ZHANG 21 Nov 24, 2022
An implementation of RetinaNet in PyTorch.

RetinaNet An implementation of RetinaNet in PyTorch. Installation Training COCO 2017 Pascal VOC Custom Dataset Evaluation Todo Credits Installation In

Conner Vercellino 297 Jan 04, 2023
Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images This repository contains the implementation of the following paper

Seonggwan Ko 9 Jul 30, 2022
Ray tracing of a Schwarzschild black hole written entirely in TensorFlow.

TensorGeodesic Ray tracing of a Schwarzschild black hole written entirely in TensorFlow. Dependencies: Python 3 TensorFlow 2.x numpy matplotlib About

5 Jan 15, 2022
App for identification of various objects. Based on YOLO v4 tiny architecture

Object_detection Repository containing trained model yolo v4 tiny, which is capable of identification 80 different classes Default feed is set to be a

Mateusz Kurdziel 0 Jun 22, 2022
Clean Machine Learning, a Coding Kata

Kata: Clean Machine Learning From Dirty Code First, open the Kata in Google Colab (or else download it) You can clone this project and launch jupyter-

Neuraxio 13 Nov 03, 2022
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

<

Ludwig 8.7k Jan 05, 2023
Multiwavelets-based operator model

Multiwavelet model for Operator maps Gaurav Gupta, Xiongye Xiao, and Paul Bogdan Multiwavelet-based Operator Learning for Differential Equations In Ne

Gaurav 33 Dec 04, 2022
YOLOX-RMPOLY

本算法为适应robomaster比赛，而改动自矩形识别的yolox算法。 基于旷视科技YOLOX，实现对不规则四边形的目标检测 TODO 修改onnx推理模型 更改/添加标注： 1.yolox/models/yolox_polyhead.py: 1.1继承yolox/models/yolo_

3 Feb 25, 2022
VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

VSR-Transformer By Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool This paper proposes a new Transformer for video super-resolution (called VSR-Transf

Jiezhang Cao 225 Nov 13, 2022
Google-drive-to-sqlite - Create a SQLite database containing metadata from Google Drive

google-drive-to-sqlite Create a SQLite database containing metadata from Google

Simon Willison 140 Dec 04, 2022