Stars
Postgraduate Thesis: fast_lio_sam + dynamic removal (T-GRS 2024) + multi-session mapping (ICRA 2022 Kim) + object-level update + online relocalization (ICRA 2025) + real-world application (MD-LVIO)
The navigation system for omnidirectional wheel robot in real world
SOTA fast and robust ground segmentation using 3D point cloud (accepted in RA-L'21 w/ IROS'21)
A Compact LiDAR Odometry and Mapping with Dynamic Removal [ICUS 2024]
OTTFFIVE / LRAE
Forked from NKU-MobFly-Robotics/LRAELRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains, RA-L, 2024
LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains, RA-L, 2024
MoveBase3D is a plane-fitting based uneven terrain navigation framework, which allows UGV to traverse different kinds of unstructured and complex environments outdoors.
Autonomous Exploration, Construction and Update of Semantic Map in real-time
A benchmark platform for robot grasping detection, integrating awesome projects and classic grasp algorithms.
[ICRA'24] A localization system based on Block Maps (BMs) to reduce the computational load caused by maintaining large-scale maps
📖[IEEE Sensors Journal (JSEN) ] SuperVINS: A Real-Time Visual-Inertial SLAM Framework for Challenging Imaging Conditions (integrated deep learning features)
[TMECH'2024] Official codes of ”PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation“
OTTFFIVE / vlmaps
Forked from vlmaps/vlmaps[ICRA2023] Implementation of Visual Language Maps for Robot Navigation
[IEEE RA-L 2024]: Graph-based SLAM-Aware Exploration with Prior Topo-Metric Information.
OTTFFIVE / The-Eyes-Have-It
Forked from cheukcat/The-Eyes-Have-ItAn intuitive approach for 3D Occupancy Detection
[IEEE RA-L & ICRA'22] A lightweight and computationally-efficient frontend LiDAR odometry solution with consistent and accurate localization.
engcang / nano_gicp
Forked from vectr-ucla/direct_lidar_odometryNano-GICP as a module from the official github repo: [IEEE RA-L & ICRA'22] A lightweight and computationally-efficient frontend LiDAR odometry solution with consistent and accurate localization.
OTTFFIVE / D-Map
Forked from hku-mars/D-MapD-Map provides an efficient occupancy mapping approach for high-resolution LiDAR sensors.