This repository provides implementation of an incremental k-d tree for robotic applications.

LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.

[ICRA@40] MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System

[CVPR2023] DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization

Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments

Learning self-supervised traversability with navigation experiences of mobile robots: A risk-aware self-training approach (IEEE RA-L '24)

This is the official repository of LiLi-OM, a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.

Python codes for Robot, vpn keyboard control, vpn data exchange, lidar data projection, and much more!

Final Year Project

This is a C++ Uni Bonn course project for SoSe 25, which demonstrates the creation of a 3D occupancy grid map from a series of LiDAR scans taken from the 3D LiDAR sensor - Hesai XT-32, mounted on a ClearPath Husky robotic platform. It leverages modern C++17 features, the Eigen library for linear algebra ops and the Open3D library for visualisation