Code exercises for the SLAM course in 'Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving' lecture series

Robotic perception SLAM course exercises

ROS Driver for Pointgrey Ladybug Cameras

ROS 2 package of 3D lidar slam using ndt/gicp registration and pose-optimization

Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scal…

Linked Data Repository to Manage Close-Range Sensing Observations

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse m…

[IROS2022] Robust Real-time LiDAR-inertial Initialization Method.

Pytorch implementation of PCT: Point Cloud Transformer

LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

Kernel Point Convolution implemented in PyTorch

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities