YunjuanSUN's Stars
BigMoWangying/LiDAR-Iris
LiDAR Iris for Loop-Closure Detection(IROS 2020)
xieqi1/a-loam-noted
ZhuangYanDLUT/lidar_gnss_mapping
kahowang/sensor-fusion-for-localization-and-mapping
深蓝学院 多传感器定位融合第四期 学习笔记
AlexGeControl/Sensor-Fusion-for-Localization-Courseware
Multi-sensor fusion for localization courseware, 深蓝学院, China
borglab/gtsam-project-python
Project template using GTSAM + python wrapping
Light-City/CPlusPlusThings
C++那些事
MichaelGrupp/evo
Python package for the evaluation of odometry and SLAM
michaelczhou/evaluation_tools
Some tools for the evaluation of odometry and SLAM.
DerrickXuNu/Learn-Carla
Learn how to use CARLA with basic APIs
TixiaoShan/imaging_lidar_place_recognition
ICRA 2021 - Robust Place Recognition using an Imaging Lidar
kxhit/awesome-point-cloud-place-recognition
A list of papers about point cloud based place recognition, also known as loop closure detection in SLAM (processing)
lajoiepy/robust_distributed_mapper
This library is an implementation of the algorithm described in Distributed Trajectory Estimation with Privacy and Communication Constraints: a Two-Stage Distributed Gauss-Seidel Approach.
CogRob/distributed-mapper
This library is an implementation of the algorithm described in Distributed Trajectory Estimation with Privacy and Communication Constraints: a Two-Stage Distributed Gauss-Seidel Approach.
sysuxyt/RDC_SLAM
electech6/ORB_SLAM3_detailed_comments
Detailed comments for ORB-SLAM3
electech6/ORB_SLAM2_detailed_comments
Detailed comments for ORB-SLAM2 with trouble-shooting, key formula derivation, and diagrammatic drawing
chennuo0125-HIT/LIO-SAM-note
lio-sam代码注释
chennuo0125-HIT/lidar_imu_calib
automatic calibration of 3D lidar and IMU extrinsics
TixiaoShan/LIO-SAM
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
RobustFieldAutonomyLab/DiSCo-SLAM
HKUST-Aerial-Robotics/A-LOAM
Advanced implementation of LOAM
Mitchell-Lee-93/kitti-lego-loam
Easy description to run and evaluate Lego-LOAM with KITTI-data
yinwu33/lidarSLAM_learning
深蓝学院 激光slam理论与实践 作业
MagicTZ/Visual-Slam-Algorithms
The different algorithms (BA, optical flow, direct method, etc) of the SLAM system, 包含了高祥博士所写的视觉slam14讲的部分内容
ttroy50/cmake-examples
Useful CMake Examples
SFUMECJF/cmake-examples-Chinese
快速入门CMake,通过例程学习语法。在线阅读地址:https://sfumecjf.github.io/cmake-examples-Chinese/
yinwu33/visualSLAM_learning
深蓝学院 视觉SLAM基础与VIO进阶 作业
shuttworth/Record_Issues_For_LVI-SAM_detailed_comments
wykxwyc/LeGO-LOAM_NOTED
LeGO-LOAM代码注释与学习