{"id":76792,"url":"https://github.com/electech6/awesome-slam","name":"awesome-slam","description":"SLAM code, paper, project collections","projects_count":74,"last_synced_at":"2026-07-24T05:00:24.499Z","repository":{"id":112157454,"uuid":"233023358","full_name":"electech6/Awesome-SLAM","owner":"electech6","description":"SLAM code, paper, project collections","archived":false,"fork":false,"pushed_at":"2020-02-10T08:59:33.000Z","size":11,"stargazers_count":47,"open_issues_count":0,"forks_count":10,"subscribers_count":2,"default_branch":"master","last_synced_at":"2026-07-05T13:04:16.322Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/electech6.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-01-10T10:26:54.000Z","updated_at":"2026-03-29T12:30:13.000Z","dependencies_parsed_at":"2023-04-05T15:34:24.241Z","dependency_job_id":null,"html_url":"https://github.com/electech6/Awesome-SLAM","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/electech6/Awesome-SLAM","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/electech6%2FAwesome-SLAM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/electech6%2FAwesome-SLAM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/electech6%2FAwesome-SLAM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/electech6%2FAwesome-SLAM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/electech6","download_url":"https://codeload.github.com/electech6/Awesome-SLAM/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/electech6%2FAwesome-SLAM/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35827727,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-07-24T02:00:07.870Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-11-09T12:20:27.001Z","updated_at":"2026-07-24T05:00:24.499Z","primary_language":"C++","list_of_lists":false,"displayable":true,"categories":["2016","ORB-SLAM2 related codes","3D reconstruction","VIO","2019","2014","2018","CAMERA-IMU-LIDAR CALIBRATION","OpenVSLAM: A Versatile Visual SLAM Framework","Line feature based SLAM","RGB-D SLAM","LIDAR-CAMERA SLAM"],"sub_categories":["lidar_slam_3d","Fastfusion","OpenVINS, Monolar + IMU","LIO-mapping","A-LOAM","SuMa++","LOAM","改进方法","LeGO-LOAM","The Kalibr visual-inertial calibration toolbox","PL-SLAM","Bundle Adjusted Direct RGB-D SLAM","PL-VIO","LIMO","loam_velodyne","Intrinsic3D","Different platforms","Monocular ORB-SLAM with Line Features","Line feature based RGBD-SLAM v2","3D Line Semi-dense SLAM","3D Line-based Stereo SLAM","hdl_graph_slam","2D LaserSLAM","Open3D","DenseSurfelMapping","StaticFusion"],"readme":"# Awesome-SLAM\nSLAM code, paper, project collections\n\n\n\n\n## ORB-SLAM2 related codes\n\nRaúl Mur-Artal and Juan D. Tardós. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics,2017\n\n[PAPER](https://arxiv.org/abs/1610.06475),\n[CODE](https://github.com/raulmur/ORB_SLAM2),\n[中文注释版](https://github.com/Vincentqyw/ORB-SLAM2-CHINESE)\n\n### 改进方法\n\n[ORBSLAM2_with_pointcloud_map](https://github.com/gaoxiang12/ORBSLAM2_with_pointcloud_map)\n\n[ORB-SLAM2_RGBD_DENSE_MAP,modified from Xiang Gao's \"ORB_SLAM2_modified\".It is added a dense loopclosing map model](https://github.com/tiantiandabaojian/ORB-SLAM2_RGBD_DENSE_MAP)\n\n[ORB-YGZ-SLAM, average 3x speed up and keep almost same accuracy v.s. ORB-SLAM2, use direct tracking in SVO to accelerate the feature matching](https://github.com/gaoxiang12/ORB-YGZ-SLAM)\n\n[YGZ-stereo-inertial SLAM, LK optical flow + sliding window bundle adjustment](https://github.com/gaoxiang12/ygz-stereo-inertial)\n\n[VIORB, An implementation of Visual Inertial ORBSLAM based on ORB-SLAM2](https://github.com/jingpang/LearnVIORB)\n\n[Fisheye-ORB-SLAM, A real-time robust monocular visual SLAM system based on ORB-SLAM for fisheye cameras, without rectifying or cropping the input images](https://github.com/lsyads/fisheye-ORB-SLAM)\n\n[Save and load orb-slam2 maps](https://github.com/AlejandroSilvestri/osmap)\n\n[ORB_SLAM2 with map load/save function](https://github.com/Jiankai-Sun/ORB_SLAM2_Enhanced)\n\n[Viewer for maps from ORB-SLAM2 Osmap](https://github.com/AlejandroSilvestri/Osmap-viewer)\n\n[ORB_SLAM2_SSD_Semantic, 动态语义SLAM 目标检测+VSLAM+光流/多视角几何动态物体检测+octomap地图+目标数据库](https://github.com/Ewenwan/ORB_SLAM2_SSD_Semantic)\n\n[Add line feature based ORB-SLAM2](https://github.com/atlas-jj/ORB_Line_SLAM)\n\n[RGBD-SLAM with Point and Line Features, developed based on ORB_SLAM2](https://github.com/maxee1900/RGBD-PL-SLAM)\n\n### Different platforms\n[Windows version ORBSLAM2,Easy built by visual studio](https://github.com/phdsky/ORBSLAM24Windows)\n\n[ORB-SLAM-Android, test on Sony Xperia Z](https://github.com/castoryan/ORB-SLAM-Android)\n\n[ORBSLAM2 on Mac OSX](https://github.com/meiroo/ORBSLAM2-OSX)\n\n[ROS interface for ORBSLAM2](https://github.com/ethz-asl/orb_slam_2_ros)\n\n\n\n\n\n\n\n## Line feature based SLAM\n\n### PL-SLAM\nStereo visual SLAM using both point and line segment features\n\n2017,PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments\n\n[PAPER](https://arxiv.org/abs/1705.09479),\n[CODE](https://github.com/rubengooj/pl-slam)\n\n2016 IROS, PL-SVO: Semi-direct monocular visual odometry by combining points and line segments.\n\n[PAPER](http://mapir.isa.uma.es/rgomez/publications/iros16plsvo.pdf)\n\n2016 ICRA, Robust stereo visual odometry through a probabilistic combination of points and line segments. In Robotics and Automation \n\n[PAPER](http://mapir.isa.uma.es/rgomez/publications/icra16plsvo.pdf)\n\n### PL-VIO\nTightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features\n\n[CODE](https://github.com/HeYijia/PL-VIO)\n\n### Monocular ORB-SLAM with Line Features\n\n[CODE](https://github.com/lanyouzibetty/ORB-SLAM2_with_line)\n\n### ORB_Line_SLAM\nAdd line feature based ORB-SLAM2\n\n[CODE](https://github.com/atlas-jj/ORB_Line_SLAM)\n\n### RGBD-PL-SLAM\nRGBD-SLAM with Point and Line Features, developed based on the famous ORB_SLAM2\n\n[CODE](https://github.com/maxee1900/RGBD-PL-SLAM)\n\n### Line feature based RGBD-SLAM v2\n\n[CODE](https://github.com/yan-lu/LineSLAM)\n\n### 3D Line Semi-dense SLAM\nICPR 2018, Incremental 3D Line Segment Extraction for Surface Reconstruction from Semi-dense SLAM\n\n[PAPER](https://arxiv.org/abs/1708.03275),\n[](http://webdocs.cs.ualberta.ca/~vis/thesis_shida/),\n[CODE](https://github.com/shidahe/semidense-lines)\n\n### 3D Line-based Stereo SLAM\n2015 IEEE Transactions on Robotics, Building a 3-D Line-Based Map Using Stereo SLAM\n\n[CODE](https://github.com/slslam/slslam),\n[论文解读](https://www.cnblogs.com/tweed/p/11353285.html)\n\n\n\n# LIDAR SLAM\n\n## 2019\n\n### hdl_graph_slam\n3D LIDAR-based Graph SLAM, real-time 6DOF SLAM using a 3D LIDAR\n2019, Advanced Robotic Systems, [A Portable 3D LIDAR-based System for Long-term and Wide-area People Behavior Measurement, Advanced Robotic Systems\n\n[PAPER](https://www.researchgate.net/publication/331283709_A_portable_three-dimensional_LIDAR-based_system_for_long-term_and_wide-area_people_behavior_measurement)\n\n### LIO-mapping\nICRA 2019,A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach\n\n[PAPER](https://arxiv.org/abs/1904.06993),\n[CODE](https://github.com/hyye/lio-mapping)\n\n### SuMa++\nIROS 2019, SuMa++: Efficient LiDAR-based Semantic SLAM\n\n[PAPER](https://www.semanticscholar.org/paper/SuMa-%2B-%2B-%3A-Efficient-LiDAR-based-Semantic-SLAM-Chen-Palazzolo/546137c71dc3c00c8a259de721281930f6574086),\n[CODE](https://github.com/PRBonn/semantic_suma)\n\n### A-LOAM\nAdvanced implementation of LOAM, uses Eigen and Ceres Solver to simplify code structure. This code is modified from LOAM and LOAM_NOTED\n\n[CODE](https://github.com/HKUST-Aerial-Robotics/A-LOAM)\n\n\n## 2018\n\n### LeGO-LOAM\nIROS 2018, LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain\n\n[CODE](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM),\n[PAPER](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM/blob/master/Shan_Englot_IROS_2018_Preprint.pdf),\n[Video demo](https://www.youtube.com/watch?v=O3tz_ftHV48),\n[Gif demo](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM/blob/master/LeGO-LOAM/launch/demo.gif)\n\n### loam_velodyne\nLaser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar\n\n[CODE](https://github.com/laboshinl/loam_velodyne)\n\n### lidar_slam_3d\na ROS package for real-time 3D slam. It is based on NDT registration algorithm. With loop detection and back-end optimization, a map with global consistency can be generated.\n\n[CODE](https://github.com/ningwang1028/lidar_slam_3d)\n\n\n\n## 2016\nGoogle Cartographer\n Real-time 2D and 3D SLAM across multiple platforms and sensor configurations.\n \n Real-Time Loop Closure in 2D LIDAR SLAM, in Robotics and Automation (ICRA), 2016 IEEE International Conference on. IEEE, 2016\n\n[PAPER](https://research.google/pubs/pub45466/),  [CODE](https://github.com/googlecartographer/cartographer),\n[PROJECT](https://google-cartographer.readthedocs.io/en/latest/)\n\n### 2D LaserSLAM\nW.Hess, D.Kohler, H.Rapp and D.Andor. Real-Time Loop Closure in 2D LIDAR SLAM. ICRA, 2016\n\n[CODE](https://github.com/meyiao/LaserSLAM),\n[VIDEO](https://v.qq.com/x/page/q0363h0i1ej.html)\n\n## 2014\n### LOAM\nLaser Odometry And Mapping (LOAM) SLAM ROS package for 3D Velodyne VLP-16 laser scanner\n\nJ. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014\n\n[PAPER](http://www.roboticsproceedings.org/rss10/p07.pdf),\n[VIDEO](https://www.youtube.com/watch?feature=player_embedded\u0026v=8ezyhTAEyHs),\n[CODE](https://github.com/daobilige-su/loam_velodyne),\n[中文注释代码](https://github.com/cuitaixiang/LOAM_NOTED)\n\n\n## LIDAR-CAMERA SLAM\n\n### LIMO\n\n2018 IROS, LIMO: Lidar-Monocular Visual Odometry\n\n[PAPER](https://arxiv.org/pdf/1807.07524.pdf),\n[CODE](https://github.com/johannes-graeter/limo)\n\n## VIO\n### OpenVINS, Monolar + IMU\nAn open source platform for visual-inertial navigation research.\n\n2019 IROS, OpenVINS: A Research Platform for Visual-Inertial Estimation\n\n[PAPER](http://udel.edu/~ghuang/iros19-vins-workshop/papers/06.pdf),\n[CODE](https://github.com/rpng/open_vins),\n[PROJECT](https://docs.openvins.com/)\n\n\n\n\n## OpenVSLAM: A Versatile Visual SLAM Framework\n\n1. A monocular, stereo, and RGBD visual SLAM system\n2. Created maps can be stored and loaded\n\n[PAPER](https://arxiv.org/abs/1910.01122),\n[CODE](https://github.com/xdspacelab/openvslam),\n[VIDEO DEMO](https://www.youtube.com/watch?v=Ro_s3Lbx5ms)\n\n## 3D reconstruction\n\n### Fastfusion\n2014 ICRA, Volumetric 3D Mapping in Real-Time on a CPU\n\n[PAPER](https://github.com/tum-vision/fastfusion),\n[CODE](http://vision.in.tum.de/_media/spezial/bib/steinbruecker_etal_icra2014.pdf),\n[Python wrapper](https://github.com/y-j-n/pyFastfusion)\n\n### Intrinsic3D\nObtain high-quality 3D reconstructions from low-cost RGB-D sensors (with poses). The algorithm recovers fine-scale geometric details and sharp surface textures by simultaneously optimizing for reconstructed geometry, surface albedos, camera poses and scene lighting.\n\n2017 ICCV, High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting\n\n[PAPER](https://arxiv.org/pdf/1708.01670.pdf),\n[CODE](https://github.com/NVlabs/intrinsic3d)\n\n### Open3D\nOpen3D: A Modern Library for 3D Data Processing, Support C++, Python\n\n[Tutorial:A complete pipeline to reconstruct a 3D scene from an RGBD sequence](http://www.open3d.org/docs/release/tutorial/ReconstructionSystem/index.html)\n\n[CODE](https://github.com/intel-isl/Open3D)\n\n\n\n### DenseSurfelMapping\n\n\nGiven a sequence of depth images, intensity images, and camera poses, the proposed methods can fuse them into a globally consistent model using surfel representation. The fusion method supports both ORB-SLAM2 and VINS-Mono.\n\n2019 ICRA, Real-time Scalable Dense Surfel Mapping\n\n[PAPER](https://www.dropbox.com/s/h9bais2wnw1g9f0/root.pdf?dl=0),\n[CODE](https://github.com/HKUST-Aerial-Robotics/DenseSurfelMapping)\n\n\n## RGB-D SLAM\n\n### Bundle Adjusted Direct RGB-D SLAM\nCVPR 2019, \"BAD SLAM: Bundle Adjusted Direct RGB-D SLAM\".\n\n[PAPER](http://openaccess.thecvf.com/content_CVPR_2019/html/Schops_BAD_SLAM_Bundle_Adjusted_Direct_RGB-D_SLAM_CVPR_2019_paper.html),\n[CODE](https://github.com/ETH3D/badslam)\n\n### StaticFusion\nBasis for ElasticFusion.\n\n2018 ICRA, StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments\n\n[CODE](https://github.com/raluca-scona/staticfusion)\n\n## CAMERA-IMU-LIDAR CALIBRATION\n\n### The Kalibr visual-inertial calibration toolbox\n\n[CODE](https://github.com/ethz-asl/kalibr)\n\n\n\n\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/electech6%2Fawesome-slam/projects"}