lianshangni0922's repositories
line_matching
A KLT-based Line Segment Matching Algorithm.
ndtpso_slam
ROS package for NDT-PSO, a 2D Laser scan matching algorithm for SLAM
SC-LIO-SAM
LiDAR-inertial SLAM: Scan Context + LIO-SAM
lili-om
LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.
removert
Remove then revert (IROS 2020)
PL-VINS
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line
smart_obstacle_layer
This is a movebase costmap layer, it's developed by mwu412
neo_local_planner
Implements a Neobotix custom move_base local planner
ORB_SLAM3
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
LIO-SAM
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
CCPD
[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition
Eigen_Handbook
A handbook to help developers get started with Eigen quickly.
localization_robot_infantry
sensors: Consumer GPS, 16 line lidar, Consumer IMU, wheel Odometry
LidarObstacleDetection
Lidar Obstacle Detection
SLAMPaperReading
线下SLAM论文分享资料
lio-mapping
Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)
scan_map_icp
use icp algorithm to match scan and map , publish initialpose to amcl to relocation in mobile robot navigation
modifiled_ekf
modifiled the robot_pose_ekf
AiLearning
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Laser_SLAM_Homework
These are useful learning materials corresponding to laser SLAM, enjoy it!
Visual-Odometry-Review
SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects
VI-Stereo-DSO
Direct sparse odometry combined with stereo cameras and IMU
limo
Lidar-Monocular Visual Odometry