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

JKK - Vehicle Industry Research Center

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

GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

GndNet: Fast Ground plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Authors: Anshul Paigwar, Ozgur Erkent, David Sierra Gonzale

114 Dec 29, 2022

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th place solution

Lyft Motion Prediction for Autonomous Vehicles Code for the 4th place solution of Lyft Motion Prediction for Autonomous Vehicles on Kaggle. Discussion

44 Jun 27, 2022

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Welcome to AirSim AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open

13.8k Jan 5, 2023

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes Introduction This is the unofficial code of Deep Dual-re

113 Dec 23, 2022

Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

BEVNet Datasets Datasets should be put inside data/. For example, data/semantic_kitti_4class_100x100. Training BEVNet-S Example: cd experiments bash t

24 Dec 12, 2022

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving (AAAI2021). RTS3D is efficiency and accuracy s

71 Nov 29, 2022

Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

RTM3D-PyTorch The PyTorch Implementation of the paper: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving (ECCV 2020

271 Nov 29, 2022

Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving Abstract In this paper, we introduce SalsaNext f

308 Jan 4, 2023

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time The first Lidar-only odometry framework with high performance based on tr

183 Dec 1, 2022

Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

LiDAR fog simulation Created by Martin Hahner at the Computer Vision Lab of ETH Zurich. This is the official code release of the paper Fog Simulation

110 Dec 30, 2022

Real-Time-Student-Attendence-System - Real Time Student Attendence System

Real-Time-Student-Attendence-System The Student Attendance Management System Pro

1 Feb 15, 2022

Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

Deep Reinforcement Learning for Smart Cities Documentation RLlib: https://docs.ray.io/en/master/rllib.html Mesa: https://mesa.readthedocs.io/en/stable

1 May 15, 2022

Yolo algorithm for detection + centroid tracker to track vehicles

Vehicle Tracking using Centroid tracker Algorithm used : Yolo algorithm for detection + centroid tracker to track vehicles Backend : opencv and python

6 Dec 21, 2022

Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.

ONNX-HybridNets-Multitask-Road-Detection Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONN

45 Jan 1, 2023

This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their

8 May 22, 2022

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

GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th place solution

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time

Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

Real-Time-Student-Attendence-System - Real Time Student Attendence System

Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

Yolo algorithm for detection + centroid tracker to track vehicles

Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.

This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

A public available dataset for road boundary detection in aerial images

Road Crack Detection Using Deep Learning Methods

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

PyTorch implementation of Memory-based semantic segmentation for off-road unstructured natural environments.

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

GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th place solution

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time

Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

Real-Time-Student-Attendence-System - Real Time Student Attendence System

Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

Yolo algorithm for detection + centroid tracker to track vehicles

Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.

This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.

A public available dataset for road boundary detection in aerial images

Road Crack Detection Using Deep Learning Methods

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

PyTorch implementation of Memory-based semantic segmentation for off-road unstructured natural environments.

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