Lidar Point Cloud
Showing 8 of 8 projects
A PyTorch implementation for super fast and accurate 3D object detection using LiDAR point clouds, featuring an anchor-free approach.
A modular C++ and ROS 2 framework for building configurable LiDAR odometry and SLAM pipelines.
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
A Python implementation for fully automatic extrinsic calibration of 3D LiDAR and cameras using laser reflectance intensity.
A full LiDAR SLAM system for static environment mapping using LiDAR with optional GPS, IMU, and odometry support.
Automatically classifies and labels urban point clouds using data fusion with public datasets and region growing techniques.
A ROS 2 node for real-time LiDAR ground segmentation using a two-phase grid-based algorithm for robotic perception.
A high-precision, grid-based C++ library for ground segmentation in LiDAR point clouds, designed for safety-critical autonomous driving and robotics.
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