大牛们的网站
//tum 实验室
https://vision.in.tum.de/research/vslam
//浙大cad
http://www.cad.zju.edu.cn/home/gfzhang/
https://github.com/zju3dv/ //章国峰
https://github.com/zju3dv/EIBA
//slam学习网站
https://www.pianshen.com/article/9300469564/
1、Hatem Alismail：
http://www.cs.cmu.edu/~halismai/research.html
http://www.cs.cmu.edu/~halismai/code.html
2、slam教程相关
https://github.com/HustRobot/VSLAM
gstam 基于因子图优化的SLAM后端库
https://github.com/borglab/gtsam
3、LSD-SLAM
https://vision.in.tum.de/research/vslam/lsdslam
https://github.com/tum-vision/lsd_slam
https://en.ids-imaging.com/store/products/cameras/page/4/sort-by/position/sort-direction/desc.html //相机
https://en.ids-imaging.com/ensenso-3d-camera-n-series.html // 3d 相机
4、LDSO （带回环的DSO）
https://github.com/tum-vision/LDSO
DSO
https://github.com/JakobEngel/dso
5、slam 教程
https://github.com/liulinbo/slam
6、maplab
https://github.com/ethz-asl/maplab ///RGB-D
https://introlab.3it.usherbrooke.ca/mediawiki-introlab/index.php/RTAB-Map
7、ElasticFusion
https://github.com/mp3guy/ElasticFusion ///RGB-D
Real-time dense visual SLAM system capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments explored using an RGB-D camera.
https://github.com/RobustFieldAutonomyLab/LeGO-LOAM ///3d lidar
This repository contains code for a lightweight and ground optimized lidar odometry and mapping (LeGO-LOAM) system for ROS compatible UGVs
The system takes in point cloud from a Velodyne VLP-16 Lidar (palced horizontally) and optional IMU data as inputs. It outputs 6D pose estimation in real-time. A demonstration of the system can be found here -> https://www.youtube.com/watch?v=O3tz_ftHV48
9、hdl_graph_slam
https://github.com/koide3/hdl_graph_slam
hdl_graph_slam is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud). We have tested this package with Velodyne (HDL32e, VLP16) and RoboSense (16 channels) sensors in indoor and outdoor environments.
10、OKVIS
https://github.com/ethz-asl/okvis
11、LIO-SAM
https://github.com/TixiaoShan/LIO-SAM /// imu Microstrain 3DM-GX5-25
12、A-LOAM
https://github.com/HKUST-Aerial-Robotics/A-LOAM
A-LOAM is an Advanced implementation of LOAM (J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), which uses Eigen and Ceres Solver to simplify code structure. This code is modified from LOAM and LOAM_NOTED. This code is clean and simple without complicated mathematical derivation and redundant operations. It is a good learning material for SLAM beginners.
13、OpenVINS
https://docs.openvins.com/getting-started.html
Welcome to the OpenVINS project! The OpenVINS project houses some core computer vision code along with a state-of-the art filter-based visual-inertial estimator. The core filter is an Extended Kalman filter which fuses inertial information with sparse visual feature tracks. These visual feature tracks are fused leveraging the Multi-State Constraint Kalman Filter (MSCKF) sliding window formulation which allows for 3D features to update the state estimate without directly estimating the feature states in the filter. Inspired by graph-based optimization systems, the included filter has modularity allowing for convenient covariance management with a proper type-based state system. Please take a look at the feature list below for full details on what the system supports.
14、dvo_slam
https://github.com/tum-vision/dvo_slam
These packages provide an implementation of the rigid body motion estimation of an RGB-D camera from consecutive images.
15、TEASER++: fast & certifiable 3D registration
TEASER++ is a fast and certifiably-robust point cloud registration library written in C++, with Python and MATLAB bindings.
https://github.com/MIT-SPARK/TEASER-plusplus
16、cartographer
https://github.com/cartographer-project/cartographer
Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.
17、orbslam3
https://github.com/UZ-SLAMLab/ORB_SLAM3
18、vins
https://github.com/HKUST-Aerial-Robotics/VINS-Mono
https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
19、ESVO //event camera
https://github.com/HKUST-Aerial-Robotics/ESVO
20、MSCKF_VIO
https://github.com/KumarRobotics/msckf_vio
The MSCKF_VIO package is a stereo version of MSCKF. The software takes in synchronized stereo images and IMU messages and generates real-time 6DOF pose estimation of the IMU frame.
21、PL_VIO
https://github.com/HeYijia/PL-VIO
