😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.

A list of references on lidar point cloud processing for autonomous driving

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

Lane and obstacle detection for active assistance during driving. Uses windowed sweep for lane detection. Combination of object tracking and YOLO for obstacles. Determines lane change, relative velocity and time to collision

The research project based on Semantic KITTTI dataset, 3d Point Cloud Segmentation , Obstacle Detection

Processing an Image to find obstacles and the minimum path between two similar objects using OpenCV.

Sensor Fusion Nanodegree Project

Dataset and Evaluation Scripts for Obstacle Detection via Semantic Segmentation in a Marine Environment

A PyTorch reimplementation of the WaSR obstacle segmentation model

ROS obstacle detection for 3D point clouds using a height map algorithm.

An obstacle tracking ROS package for detecting obstacles using 2D LiDAR scan using an Extended object tracking algorithm

Project: Lidar Obstacle Detection || Udacity: Sensor Fusion Engineer Nanodegree

This program detect and identify obstacle on railway. If program detect some obstacle that train must stop, program gives you warning sign. This program Also estimate riskiness of obstacle how it is negligible or not. We provide many models to you to detect railways and obstacles.

Obstacle detection using lidar point cloud

Temporal WaSR-T model for maritime obstacle detection via semantic segmentation

3D LiDAR obstacle detection on point cloud data using segmentation and clustering.

ROS package for obstacle segmentation in a point cloud scene

This package is essentially a ros-wrapper of neural_cam. More features would be added in the future, geared towards mobile robot platform. Eventual goal of this package is to solve the problem of given an image, where is the obstacle with respect to the robot in a 3D space.

In this project I aim to develop an unsupervised sense-and-avoid system for UAVs using sparse and dense optical flow.