h-k8888's repositories
book-cpp-algorithms
The Standard Algorithms in C++.
LOCUS2.0
Robust Lidar Odometry System
M2DGR
M2DGR： a Multi-modal and Multi-scenario Dataset for Ground Robots
PL-VINS
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features
UV-SLAM
Official page of UV-SLAM (RA-L with ICRA2022 option)
LTSLAM
You can learn slam step by step，there are lot of tutorials
range-mcl
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps (chen2021icra)
livox_camera_calib
This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
Kimera-Semantics
Real-Time 3D Semantic Reconstruction from 2D data
direct_lidar_odometry
Direct LiDAR Odometry: Fast Localization with Dense Point Clouds
ICRA-2022-SLAM-paper-list
Unofficial ICRA 2022 SLAM paper list
PL-VIO
monocular visual inertial system with point and line features
LineTR
Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localization
Kimera-VIO-ROS
ROS wrapper for Kimera-VIO
LVI-SAM-noted
LVI-SAM中文注释。Chinese notes of LVI-SAM
LIMO-Velo
A real-time, direct and tightly-coupled LiDAR-Inertial SLAM for high velocities with spinning LiDARs
LiDAR_IMU_Init
Robust and Online LiDAR-inertial Initialization Method.
faster-lio
Faster-LIO: Lightweight Tightly Coupled Lidar-inertial Odometry using Parallel Sparse Incremental Voxels
VIW-Fusion
Visual-inertial-wheel fusion odometry, better performance in scenes with drastic changes in light
clins
CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System
r3live_noted
R3LIVE中文注释。Chinese notes of R3LIVE
r3live
A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
ct_icp
Continuous Time LiDAR odometry
Three-Filters-to-Normal
Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator (RAL+ICRA'21)
FAST_LIO_NOTED
Chinese annotation. For study.如有错误欢迎指正
Livox-Mapping
An all-in-one and ready-to-use LiDAR-inertial odometry system for Livox LiDAR
open_vins
An open source platform for visual-inertial navigation research.
r2live
R2LIVE is a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses the measurement from the LiDAR, inertial sensor, visual camera to achieve robust, accurate state estimation.