/lidar_obstacle_detector

3D LiDAR Object Detection & Tracking using Euclidean Clustering, RANSAC, & Hungarian Algorithm

Primary LanguageC++MIT LicenseMIT

lidar_obstacle_detector

3D LiDAR Object Detection & Tracking using Euclidean Clustering & Hungarian algorithm

Ubuntu ROS C++ CodeFactor ROS-CI cpp-linter GitHub Repo stars GitHub Repo forks

demo_1

demo_2

Features

  • Segmentation of ground plane and obstacle point clouds
  • Customizable Region of Interest (ROI) for obstacle detection
  • Customizable region for removing ego vehicle points from the point cloud
  • Tracking of obstacles between frames using IOU gauge and Hungarian algorithm
  • In order to help you tune the parameters to suit your own applications better, all the key parameters of the algorithm are controllable in live action using the ros param dynamic reconfigure feature

TODOs

  • LiDAR pointcloud motion undistortion
  • Drive Space/Kurb Segmentation
  • Refine PCA Bounding Boxes by L-Shape fitting
  • Add trackers such as UKF

Known Issues

  • PCA Bounding Boxes might not be accurate in certain situations

Dependencies

  • autoware-msgs
  • jsk-recognition-msgs

Installation

# clone the repo
cd catkin_ws/src
git clone https://github.com/SS47816/lidar_obstacle_detector.git

# install dependencies & build
cd ..
rosdep install --from-paths src --ignore-src -r -y
catkin_make # or catkin_make -DPYTHON_EXECUTABLE=/usr/bin/python3
source devel/setup.bash

Usage

1. (Easy) Use this pkg with ROS Bags (mai_city dataset as an example here)

demo_mai_city

Step 1: Download the mai_city dataset from their Official Website

Step 2: Launch the nodes using the mai_city.launch launch file

# this will launch the obstacle_detector node, rviz, and rqt_reconfigure GUI together
roslaunch lidar_obstacle_detector mai_city.launch

Step 3: Run any of the bags from the dataset

# go to the folder where the dataset is located
cd mai_city/bags
# play the rosbag
rosbag play 00.bag

2. Use this pkg with LGSVL Simulator (with the help of the lgsvl_utils pkg)

demo_lgsvl

Step 1: Launch the LGSVL simulator and lgsvl_utils nodes

Please refer this step to the README Usage Section of the lgsvl_utils pkg

Step 2: Launch the nodes using the launch/lgsvl.launch launch file

# launch node
roslaunch lidar_obstacle_detector lgsvl.launch

Contribution

You are welcome contributing to the package by opening a pull-request

We are following: Google C++ Style Guide, C++ Core Guidelines, and ROS C++ Style Guide

License

MIT License