2021年开源SLAM算法

明尼苏达大学，快速准确的三维视觉SLAM，不依赖特征探测和匹配。从单目DSO扩展到双目系统，通过3D最小光度误差优化双目配置尺度。 项目地址： Interactive Robotics and Vision Lab 论文地址：https://arxiv.org/pdf/2112.01890.pdf 源码地址：https://github.com/IRVLab/direct_stereo_slam 相关工作：Direct Sparse Odometry，A Photometrically Calibrated Benchmark For Monocular Visual Odometry， https://github.com/JakobEngel/dso

沈邵杰团队，香港科技大学，以前开源VINS-Mono(https://github.com/HKUST-Aerial-Robotics/VINS-Mono)，VINS-Fusion(https://github.com/HKUST-Aerial-Robotics/VINS-Fusion)，GVINS是基于GNSS、估计视觉和惯导紧耦合多传感器融合平滑一致。

基于伦敦大学ORB-SLAM2.面向对象的语义SLAM 项目地址：https://jingwenwang95.github.io/dsp-slam/

论文地址：https://arxiv.org/pdf/2108.09481v2.pdf 源码地址：https://github.com/JingwenWang95/DSP-SLAM

爱丁堡大学、旷视科技，大规模BA算法 ，GPU分布式计算项目地址： 论文地址：https://arxiv.org/pdf/2112.01349.pdf，https://arxiv.org/pdf/2112.01349v2.pdf 源码地址：https://github.com/MegviiRobot/MegBA

慕尼黑工业大学Daniel Cremers团队半监督单目密集重建纯视觉SLAM 项目地址：https://vision.in.tum.de/research/monorec 论文地址：https://arxiv.org/pdf/2011.11814.pdf 源码地址：https://github.com/Brummi/MonoRec

项目地址：https://www.ipb.uni-bonn.de/research/，https://www.ipb.uni-bonn.de/data-software/ 论文地址：https://arxiv.org/pdf/2105.12121.pdf 源码地址：https://github.com/PRBonn/range-mcl

苏黎世联邦理工学院EPFL洛桑联邦理工学院、禾赛科技 激光SLAM项目地址：https://baug.ethz.ch/en/，https://www.hesaitech.com/zh/，https://sti.epfl.ch/ 论文地址：https://arxiv.org/pdf/2102.03771,https://arxiv.org/pdf/2102.03771v3 源码地址：https://github.com/YuePanEdward/MULLS

德国卡尔斯鲁厄理工学院，实时紧耦合激光雷达惯性里程计SLAM，特征提取参考固态激光雷达 Livox Horizon 与机械激光雷达Velodyne A-LOAM( HKUST-Aerial-Robotics )，可先参考开源VINS-Fusion(https://github.com/HKUST-Aerial-Robotics/VINS-Fusion)和 LIO-mapping(https://github.com/hyye/lio-mapping)。 项目地址： https://isas.iar.kit.edu/ 论文地址：https://arxiv.org/pdf/2010.13150.pdf,https://arxiv.org/pdf/2010.13150v3 源码地址：https://github.com/KIT-ISAS/lili-om

香港大学张富团队，在FAST-LIO(高效鲁棒性LiDAR、惯性里程库，融合LiDAR特征点和IIMU数据，紧耦合快速EKF迭代)基础上采用ikd-Tree(https://github.com/hku-mars/ikd-Tree)增量建图，原始LiDAR点直接计算里程，支持外部IMU，并支持ARM平台。 项目地址：https://mars.hku.hk/ 论文地址：https://arxiv.org/pdf/2107.06829.pdf，https://arxiv.org/pdf/2107.06829v1.pdf，https://arxiv.org/pdf/2010.08196v3.pdf 源码地址：https://github.com/hku-mars/FAST_LIO 相关工作 ikd-Tree: A state-of-art dynamic KD-Tree for 3D kNN search. https://github.com/hku-mars/ikd-TreeIKFOM: A Toolbox for fast and high-precision on-manifold Kalman filter. https://github.com/hku-mars/IKFoM UAV Avoiding Dynamic Obstacles: One of the implementation of FAST-LIO in robot’s planning.https://github.com/hku-mars/dyn_small_obs_avoidance R2LIVE: A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.https://github.com/hku-mars/r2live UGV Demo: Model Predictive Control for Trajectory Tracking on Differentiable Manifolds.https://www.youtube.com/watch?v=wikgrQbE6Cs FAST-LIO-SLAM: The integration of FAST-LIO with Scan-Context loop closure module.https://github.com/gisbi-kim/FAST_LIO_SLAM FAST-LIO-LOCALIZATION: The integration of FAST-LIO with Re-localization function module.https://github.com/HViktorTsoi/FAST_LIO_LOCALIZATION

香港大学张富团队，在R2LIVE(FAST-LIO与VIO)基础上，LiDAR、惯导、视觉多传感器融合SLAM 项目地址：https://mars.hku.hk/ 论文地址：https://arxiv.org/pdf/2109.07982.pdf，https://arxiv.org/pdf/2109.07982v1.pdf 源码地址：https://github.com/hku-mars/r3live， (今天上传源代码) 相关工作 R2LIVE: A robust, real-time tightly-coupled multi-sensor fusion package.https://github.com/hku-mars/r2live FAST-LIO: A computationally efficient and robust LiDAR-inertial odometry package.https://github.com/hku-mars/FAST_LIO ikd-Tree: A state-of-art dynamic KD-Tree for 3D kNN search.https://github.com/hku-mars/ikd-Tree LOAM-Livox: A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR.https://github.com/hku-mars/loam_livox openMVS: A library for computer-vision scientists and especially targeted to the Multi-View Stereo reconstruction community.https://github.com/cdcseacave/openMVS VCGlib: An open source, portable, header-only Visualization and Computer Graphics Library.https://github.com/cnr-isti-vclab/vcglib CGAL: A C++ Computational Geometry Algorithms Library.https://www.cgal.org/,https://github.com/CGAL/cgal

MIT麻省理工学院TixiaoShan(之前开源LIO-SAM，https://github.com/TixiaoShan/LIO-SAM)，激光、视觉、惯性紧耦合多传感器融合SLAM，里程计建图系统层联合LIO-SAM与Vins-Mono优势，依赖与ROS、gtsam、Ceres库。 项目地址：https://git.io/lvi-sam，https://dusp.mit.edu/，https://senseable.mit.edu/，https://www.ams-institute.org/ 论文地址：https://arxiv.org/pdf/2104.10831.pdf，https://arxiv.org/pdf/2104.10831v2.pdf 源码地址：https://github.com/TixiaoShan/LVI-SAM

KAIST韩国科学技术院，采用消隐点实现无约束线特征结构化建图，克服传统线重投影测量模型中仅利用 Plücker坐标线法向量。 项目地址：Urban Robotics Lab论文地址：https://arxiv.org/pdf/2112.13515.pdf 源码地址：https://github.com/url-kaist/UV-SLAM， 源码即将上传 相关研究：Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features ALVIO: Adaptive Line and Point Feature-Based Visual Inertial Odometry for Robust Localization in Indoor Environments，源码未上传https://github.com/ankh88324/ALVIO

克拉克森大学， 基于slam室内导航软件架构，应用于智能轮椅 项目地址： 论文地址：https://arxiv.org/pdf/2112.07723.pdf，(OpenVSLAM)https://openvslam-community.readthedocs.io/_/downloads/en/latest/pdf/ 源码地址：https://github.com/michealcarac/VSLAM-Mapping

巴利阿里群岛大学，基于RGB-D视觉里程计，融合点与线特征，结构化约束。 项目地址： 论文地址：https://arxiv.org/pdf/2111.03408.pdf 源码地址：GitHub - joanpepcompany/MSC-VO: This repository includes an RGB-D VO system that combines both point and line features and leverages, if exist, structural regularities and the Manhattan axes of the scene.相关工作：