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UrbanNav: an Open-Sourcing Localization Data Collected in Asian Urban Canyons, Including Tokyo and Hong Kong
Fast and Communication-Efficient Multi-UAV Exploration Via Voronoi Partition on Dynamic Topological Graph
A simple localization framework that can re-localize in built maps based on FAST-LIO.
The Kalibr visual-inertial calibration toolbox
Vicon-IMU fusion for groundtruth trajectory generation.
IMU Allan standard deviation charts for use with Kalibr and inertial kalman filters.
An efficient single/multi-agent trajectory planner for multicopters.
A real-time multifunctional Lidar SLAM package.
ROS package developed for UWB positioning system and TOF lidar written in C++.
UrbanNav:An Open-sourced Multisensory Dataset for Benchmarking Positioning Algorithms Designed for Urban Areas
gisbi-kim / FAST_LIO_SLAM
Forked from hku-mars/FAST_LIOLiDAR SLAM = FAST-LIO + Scan Context
Implementations of a rather simple version of the Iterative Closest Point algorithm in various languages.
How to calibrate 3-axis magnetometers with Magneto. Includes Python scripts and an Arduino example.
Lecture page for AAE4203, Semester 1, 2024-2025
Joinied Positioning and Control Model Based on Factor Graph Optimization (FGO)
A Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry (LIVO).
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
Tightly-coupled integrated INS/UWB navigation system for UAV indoor localization
Accurate 3D Localization for MAV Swarms by UWB and IMU Fusion. ICCA 2018
MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
NVIDIA Isaac Transport for ROS package for hardware-acceleration friendly movement of messages
ROS Wrapper for Intel(R) RealSense(TM) Cameras
AprilTag is a visual fiducial system popular for robotics research.
A ROS2 package of CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms.
Benchmarking and evaluation framework for place recognition methods, featuring SuperPoint+SuperGlue, LoGG3D-Net, Scan Context, DBoW2, MixVPR, STD
强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/