A list of references on lidar point cloud processing for autonomous driving
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Updated
Apr 17, 2022
A list of references on lidar point cloud processing for autonomous driving
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
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
3D LiDAR Object Detection & Tracking using Euclidean Clustering, RANSAC, & Hungarian Algorithm
Processing an Image to find obstacles and the minimum path between two similar objects using OpenCV.
A PAL Mobile Base for your advanced navigation needs
The research project based on Semantic KITTTI dataset, 3d Point Cloud Segmentation , Obstacle Detection
Project: Lidar Obstacle Detection || Udacity: Sensor Fusion Engineer Nanodegree
A PyTorch reimplementation of the WaSR obstacle segmentation model
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.
ROS obstacle detection for 3D point clouds using a height map algorithm.
ROS package for obstacle segmentation in a point cloud scene
Temporal WaSR-T model for maritime obstacle detection via semantic segmentation
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.
Dataset and Evaluation Scripts for Obstacle Detection via Semantic Segmentation in a Marine Environment
Obstacle detection using lidar point cloud
Software and hardware for the Clover drone for make drone control easily (preventing collisions with obstacles, web setup, etc.)
My goal is to come up with a simple and a basic model of an obstacle avoiding bot with the best possible algorithm to detect and avoid an obstacle using only One Ultrasonic Sensor module (HCSR04) and 2 wheels. The project is still into development to find even better an algorithm to achieve the same task.
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