yjs's repositories
100-gdb-tips
A collection of gdb tips. 100 maybe just mean many here.
allan_variance_ros
ROS compatible tool to generate Allan Deviation plots
cartographer_detailed_comments_ws
cartographer work space with detailed comments
ccm_slam
CCM-SLAM: Robust and Efficient Centralized Collaborative Monocular SLAM for Robotic Teams
DSP-SLAM
[3DV 2021] DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
EPro-PnP
[CVPR 2022 Oral, Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
FAST_LIO
A computationally efficient and robust LiDAR-inertial odometry (LIO) package
faster-lio
Faster-LIO: Lightweight Tightly Coupled Lidar-inertial Odometry using Parallel Sparse Incremental Voxels
GVINS
Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.
leetcode_101
LeetCode 101：和你一起你轻松刷题（C++）
lili-om
LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.
LIMO-Velo
A real-time, direct and tightly-coupled LiDAR-Inertial SLAM for high velocities with spinning LiDARs
LIO-Livox
A Robust LiDAR-Inertial Odometry for Livox LiDAR
Livox-Localization
A simple localization framework that can re-localize in one point-cloud map.
Livox-Mapping
An all-in-one and ready-to-use LiDAR-inertial odometry system for Livox LiDAR
LVI-SAM
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
lvio_fusion
Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method (IROS 2021)
MULLS
MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square [ICRA '21]
ORB_SLAM3
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
PL-SLAM
PL-SLAM: The method is implemented in《PL-SLAM:Real-time Monocular Visual SLAM with Points and Lines》
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.
range-mcl
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps (chen2021icra)
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
tandem
[CoRL 21'] TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo
VIW-Fusion
Visual-inertial-wheel fusion odometry, better performance in scenes with drastic changes in light
ZhangZhengYou
张正友标定法的数学原理以及python源码实现（详细）