VeloView performs real-time visualization and easy processing of live captured 3D LiDAR data from Velodyne sensors (Alpha Prime™, Puck™, Ultra Puck™, Puck Hi-Res™, Alpha Puck™, Puck LITE™, HDL-32, HDL-64E). Runs on Windows, Linux and MacOS. This repository is a mirror of https://gitlab.kitware.com/LidarView/VeloView-Velodyne.

Lidar Obstacle Detection

LiDAR Guide

Utilized an Extended Kalman Filter and Sensor Fusion to estimate the state of a moving object of interest with noisy lidar and radar measurements. The project involved utilzing lidar data (Point Cloud) for position and radar data (Doppler) for radial velocity.

Containerized ROS node that communicates with x4 lidar via USB pass though from the docker host

Code and documents to support the Thesis: Progress Towards LiDAR Based Bicycle Detection in Urban Environments Edit Add topics

High-performance localization software for autonomous vehicles. A particle filter is combined with a map to localize a vehicle.

.net wrapper of YDLidar sdk

Slamtec RPLIDAR with a Raspberry PI

Raspberry Pi powered robot with camera live stream and 360° lidar.

Simulation of a LiDAR (Light Detection and Ranging) sensor

Implementation using only LASER (LIDAR) measurements to predict a pedestrian

A LiDAR 3D Mapping system which is very low cost at 5000₹(~80$) made using arduino.

Utilize an Extended Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements

Explore and analyze LiDAR data from Global Ecosystem Dynamics Investigation (GEDI) of NASA

Implementation of the Extended Kalman Filter in C++

Implementing Extended Kalman Filter in C++

Utilize an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements

Implemented sensor fusion algorithm using an extended Kalman filter that tracks nearby moving objects using RADAR and LIDAR measurements.