My personal list of important content related to Lifelong SLAM and Robotics.
- Related survey papers
- Related research papers
- Datasets
- Laboratories and Research Groups
- Conferences
- Journals
- Courses
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A Systematic Literature Review on Long-Term Localization and Mapping for Mobile Robots. RB Sousa. the University of Porto. 2022
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A survey: which features are required for dynamic visual simultaneous localization and mapping?. Zewen Xu,CAS. 2021
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(ISRR 2022)Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
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(IEEE RA-L'22)DynaVINS: A Visual-Inertial SLAM for Dynamic Environments, code:https://github.com/url-kaist/dynaVINS, 非深度学习结合的方案，而是使用约束对运动对象上的特征点进行去除
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POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes
- RSS 2022，半静态场景中的地图更新
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L. Sun, Z. Yan, A. Zaganidis, C. Zhao, and T. Duckett, “Recurrent-OctoMap: Learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data,” RAL
- life long slam
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P. Egger, P. V. K. Borges, G. Catt, A. Pfrunder, R. Siegwart, and R. Dubé, “PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization,” IROS 2018
- lifelong slam，ETH SAL组
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M. T. Lázaro, R. Capobianco, and G. Grisetti, “Efficient Long-term Mapping in Dynamic Environments,” IROS 2018
- 高效的ICP方案，并且实现了地图实体的合并。由于处理的是2D地图，因此也就没有那么多的需要处理的东西。可以直接用点可视化来去除运动的点云。
- code，
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T. Krajník, J. P. Fentanes, J. M. Santos, and T. Duckett, “FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments,” TRO 2017
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G. Kurz, M. Holoch, and P. Biber, “Geometry-based Graph Pruning for Lifelong SLAM.” IROS 2021
- 提出了一种新的方法，该方法考虑了几何准则来选择要剪枝的顶点。这是有效的，易于实现，并导致具有均匀分布的顶点的图形，这些顶点仍然是机器人轨迹的一部分。此外，我们提出了一种新的边际化方法，与现有方法相比，该方法对错误的循环闭包具有更强的鲁棒性。 主要设计到SLAM后端的优化，当地图或者是因子图更新时，如何对因子图进行剪枝的问题。
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W. Ding, S. Hou, H. Gao, G. Wan, and S. Song, “LiDAR Inertial Odometry Aided Robust LiDAR Localization System in Changing City Scenes,” ICRA 2020
- 百度出品的使用激光和IMU，在运动场景的定位，并且在之前构建的地图中，针对场景新增加的东西，将会新建相关的地图。
- life-long SLAM
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G. D. Tipaldi, D. Meyer-Delius, and W. Burgard, “Lifelong localization in changing environments,” IJRR 2013
- life-long的定位
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S. Zhu, X. Zhang, S. Guo, J. Li, and H. Liu, “Lifelong Localization in Semi-Dynamic Environment,” ICRA 2021
- 清华，life-long的定位
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F. Pomerleau, P. Krüsi, F. Colas, P. Furgale, and R. Siegwart, “Long-term 3D map maintenance in dynamic environments,” ICRA 2014
- 动态环境中，地图更新
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D. J. Yoon, T. Y. Tang, and T. D. Barfoot, “Mapless Online Detection of Dynamic Objects in 3D Lidar.” Conference on Computer and Robot Vision (CRV) 2019
- 点云动态检测
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M. Zhao et al., “A General Framework for Lifelong Localization and Mapping in Changing Environment.” IROS 2021
- 高仙机器人的life-long 定位的论文
- 多session的地图表示和一种高效的在线地图更新策略，子系统组成：局部激光雷达里程计（LLO）、全局激光雷达匹配（GLM）和位姿图优化（PGR），LLO的作用是构建一系列局部一致的子地图，GLM子系统负责计算传入扫描点云和全局子地图之间的相对约束，并将子映地图和约束插入PGR，PGR是系统中最重要的部分，它从LLO和GLM收集子地图和约束关系，修剪并保存在历史地图中的旧的子地图，并执行姿势图稀疏化和优化。
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Pfreundschuh, Patrick, et al. “Dynamic Object Aware LiDAR SLAM Based on Automatic Generation of Training Data.” (ICRA 2021)
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Chen Xieyuanli, et al. “Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data.” IEEE Robotics and Automation Letters, 2021
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Rosen, David M., et al. “Towards Lifelong Feature-Based Mapping in Semi-Static Environments.” (ICRA 2016)
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Luiten Jonathon, et al. “Track to Reconstruct and Reconstruct to Track.”, (RAL+ICRA 2020)
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Grinvald, Margarita, et al. “TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction.”(ICRA 2021)
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P Wenzel, R Wang, N Yang, Q Cheng,Qadeer Khan, Lukas von Stumberg, Niclas Zeller, Daniel Cremers， “4Seasons: A cross-season dataset for multi-weather SLAM in autonomous driving,” DAGM GCPR 2020
- 自动驾驶中多天气SLAM的跨季节数据集，TUM，网站
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X Shi, D Li et al. “Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM,” ICRA 2020
- 开放LORIS-场景数据集，清华大学，网站
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Yin, Jie and Li, Ang and Li, Tao and Yu, Wenxian and Zou, Danping, “M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots,” IEEE RA-L'21
- M2DGR，上海交通大学，网站
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Peng Yin and Shiqi Zhao et al. “ALITA: A Large-scale Incremental Dataset for Long-term Autonomy,” arXiv 2022
- ALITA，Carnegie Mellon University，网站
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Y. Pan, B. Gao, J. Mei, S. Geng, C. Li, and H. Zhao, “SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances,” IV 2020
- 动态物体室外数据集，北大，网站
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K. Minoda, F. Schilling, V. Wüest, D. Floreano, and T. Yairi, “VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments,”RAL 2021
- 动态环境的数据集，包括了静态，动态等级的场景，感觉适合用来作为验证。
- 东京大学，code
- AirLab | Carnegie Mellon University - Sebastian Scherer
- Autonomous Vision Group (AVG) | University of Tübingen - Andreas Geiger
- ICL Dyson Robotics Lab | Imperial College London - Andrew Davison
- Robotics and Perception Group | University of Zurich - Davide Scaramuzza GitHub Link
- Laboratrio de Robótica Móvel | ICMC/USP São Carlos - Fernando Osório
- StachnissLab | University of Bonn - Cyrill Stachniss GitHub Link
- Autonomous Intelligent Systems | University of Freiburg - Wolfram Burgard
- Multi-Robt Systems Group | University in Prague - Martin Saska
- Robotics and Intelligent Systems (ROBIN) | University of Oslo - Jim Tørresen
- Machine Perception and Intelligent Robotics (MAPIR) | University of Málaga - Javier Gonzalez Jimenez
- Robotics, Perception and Real Time Group | Universidad de Zaragoza - Juan D. Tardós and others GitHub Link
- Team CoSTAR | A collaboration between NASA’s JPL, MIT, Caltech, KAIST, LTU, and several industry partners
- Autonomous Navigation and Perception Lab (ANPL) | Technion-Israel Institute of Technology - Vadim Indelman
- Institute for Robotics and Intelligent Machines (IRIM) | Georgia Institute of Technology
- Vijay Kumar Lab | University of Pennsylvania - Vijay Kumar
- Phi Robotics | Federal University of Rio Grande do Sul - Edson Prestes
- Computer Science and Artificial Intelligence Laboratory (CSAIL) | MIT’s research lab
- Goal-Oriented Long-Lived Systems (GOALS) | Oxford Robotics Institute - Nick Hawes
- Skoltech Mobile Robotics | Skolkovo Institute of Science and Technology - Gonzalo Ferrer
- Compuver Vision and Intelligent Systems Laboratory | Ryerson University - Guanghui (Richard) Wang
- UTS Robotics Institute | University of Technology Sydney - Sarath Kodagoda
- Lab for Autonomous Robotics Research | Seoul National University - H. Jin Kim GitHub Link
- ASRL - Autonomous Space Robotics Lab | University of Toronto - Tim Barfoot GitHub Link
- Urban Robotics Lab | KAIST (Korea Advanced Institute of Science and Technology) - Hyun MYUNG GitHub Link
- ETHZ V4RL | ETH Zurich - Chli, Margarita GitHub Link
- Dynamic Vision and Learning Group | Technische Universität München - Laura Leal-Taixé GitHub Link
- Computer Vision and Geometry Lab | ETH Zurich - Marc Pollefeys GitHub Link
- MIST Laboratory | University of Montreal - Giovanni Beltrame GitHub Link
- SPARK (Sensing, Perception, and Robot Kinetics) Lab | Massachusetts Institute of Technology - Luca Carlone GitHub Link
- Stanford Vision and Learning Lab | Stanford University - Fei-Fei Li GitHub Link
- TUM Computer Vision Group | Technische Universität München - Daniel Cremers GitHub Link
- Spatial AI & Robotics Lab | State University of New York at Buffalo - Chen Wang GitHub Link
- Robot Perception Lab | Carnegie Mellon University - Michael Kaess GitHub Link
- Robotics, Perception and Learning Lab | KTH Royal Institute of Technology - Patric Jensfelt GitHub Link
- Smart Robotics Lab | Technische Universität München - Stefan Leutenegger
- UCLA VISION LAB | UCLA - Stefano Soatto GitHub Link
- Multiple Autonomous Robotic Systems (MARS) Laboratory | University of Minnesota - Stergios Roumeliotis
- Active Vision Laboratory | University of Oxford - Victor Prisacariu
- Autonomous Systems Lab (ASL) | ETH Zurich - Roland Siegwart GitHub Link
- NTNU Autonomous Robots Lab | Norwegian University of Science and Technology - Kostas Alexis GitHub Link
- ZJU3DV Lab of CAD&CG | Zhejiang University - 周晓巍 GitHub Link
- Mechatronics and Robotic Systems (MaRS) Laboratory | The University of Hong Kong - Fu Zhang GitHub Link
- Integrated and Intelligent Navigation Group | Wuhan University - 牛小骥 GitHub Link
- MARS Lab | Tsinghua University - 赵行 GitHub Link
- HKUST Aerial Robotics Group | the Hong Kong University of Science and Technology - Shaojie Shen (沈劭劼) GitHub Link
- RAPID Laboratory | Sun Yat-sen University - 成慧 GitHub Link
- MetaSLAM | City University of Hong Kong - 殷鹏 GitHub Link
- ACM/IEEE International Conference on Human Robot Interaction (HRI)
- CISM IFToMM Symposium on Robot Design, Dynamics and Control (RoManSy)
- IEEE Conference on Decision and Controls (CDC)
- IEEE International Conference on Rehabilitation Robotics (ICORR)
- IEEE International Conference on Robotics and Automation (ICRA)
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- IEEE-RAS International Conference on Humanoid Robots (Humanoids)
- International Symposium of Robotic Research (ISRR)
- International Symposium of Experimental Robotics (ISER)
- Robotics: Science and Systems Conference (RSS)
- The International Workshop on the Algorithmic Foundations of Robotics (WAFR)
- Robotics and Autonomous Systems | Elsevier - Impact factor: 2.259
- Journal of Intelligent & Robotic Systems (JINT) | Springer - Impact factor: 2.825
- Transactions on Robotics (T-RO) | IEEE - Impact factor: 6.123
- International Journal of Robotics Research (IJRR) | SAGE - Impact factor: 4.703
- Autonomous Robots | Springer - Impact factor: 3.602
- Robotics and Computer-Integrated Manufacturing | Elsevier - Impact factor: 5.057
- Journal of Field Robotics (JFR) | Wiley - Impact factor: 3.581
- Robotics & Automation Magazine (RAM) | IEEE - Impact factor: 4.250
- Robotica | Cambridge Core - Impact factor: 1.509
- Robotics and Automation Letters (RA-L) | IEEE - Impact factor: 3.6
- Mobile Sensing and Robotics | YouTube - Cyrill Stachniss
- Mobile Sensing and Robotics - 2 | YouTube - Cyrill Stachniss
- Photogrametry | YouTube - Cyrill Stachniss
- SLAM | YouTube - Cyrill Stachniss
- Modern C++ | YouTube - Cyrill Stachniss
- Become a Robotics Software Engineer | YouTube -Udacity
- Different playlists about ROS | YouTube - The Construct
- A platform to learn/teach robotics from zero | The Construct
- Autonomous Systems | Udacity
- Artificial Intelligence for Robotics | Udacity - Prof. Dr. Sebastian Thrun
- C++ Essential Training | Bill Weinman
- Slides of Computer Vision | Lectures from Cornell - 2013
- Computer Vision: Foundations and Applications | Stanford University - Prof. Fei-Fei Li
- Interval Analysis | IAMOOC - Prof. Luc Jaulin
- Robot Mapping | University of Freiburg - Prof. Cyrill Stachniss
- JAI: Robótica e Simulação com o V-REP | CSBC - Julho 2015
- Digital Image Processing | University of Tartu - Prof. Dr. Gholamreza Anbarjafari
- Tutorial on Visual Odometry | University of Zurich - Prof. Davide Scaramuzza
- Visual SLAM Tutorial | Frank Dellaert and Michael Kaess
- Visual Odometry from scratch - A tutorial for beginners | Avi Singh's blog
- Aerial Robot Courses | University of Nevada - Dr. Kostas Alexis
- Stereo Vision Tutorial | Chris McCormick
- Different ROS courses | Udemy
- Introduction to Mobile Robotics | University of Freiburg - Prof. Dr. Wolfram Burgard
- GaiTech EDU | Educational website on Robot Operating System (ROS)
- Robocademy | ROS Course from Lentin Joseph
- Hello (Real) World with ROS | Delft University of Technology - edX
- Self-Driving Cars with ROS and Autoware | The Autoware Foundation
- Programming for Robotics - ROS |ETH Zurich - Robotics System Lab
- CVML knowledge self-assessment | Aristotle University of Thessaloniki - Prof. I. Pitas
- Think Autonomous | Jeremy Cohen
- PyImageSearch | Adrian Rosebrock
- DuckieTown | Learning Autonomy
- Machine Learning | Stanford - Andrew Ng
- Deep Learning Specialization | Stanford - Andrew Ng
- OpenCV: C++ and Python | Satya Mallick
- Awesome Self-Driving | Ivan Deylid
- Principles of Computer Vision | Shree K. Nayar
- Awesome Computer Vision | Jia-Bin Huang
- Introduction to ROS: The Robot Operating System | Tiziano's school
- ROS Tutorials | Erle Robotics
- ROS Industrial Training | Industrial Training Exercises
- ROS C++ Hello World | Jonathan Bohren
- ROS2 C++ Hello World | Jonathan Bohren
- LearnOpencv | Satya Mallick
- Programming for Robotics | Acshi Haggenmiller (acshikh)
- Mathematics for Robotics | University of Michigan
- Introduction to the A* Algorithm | Red Blob Games
- Computer Science courses with video lectures | Developer-Y
- Common Path Planning Algorithms | KamalanathanN
- PyVisualOdometry | Polygon-Software
- KITTI Odometry in Python and OpenCV | Nate Cibik
- Awesome 3D Reconstruction List | OpenMVG
- ROS Autonomous Driving and Path Planning SLAM with TurtleBot3 | Muhammad Luqman
- ROS2 Ultimate learners Repository with Supporting Documentation | Muhammad Luqman
- ROS2 C++ learning | Antonio Mauro Galiano
- F1TENTH | F1TENTH Autonomous Racing Community
- ROS Industrial Training | ROS Industrial
- ROS Book Sample Code | Programming Robots with ROS
- CPP Best Practices | CPP Best Practices
- C++ Cheatsheet | Modern C++ Cheatsheet
- Computer Vision | Prof. Kosta Derpanis
- Machine Learning Course Notes | DAIR.AI
- Data Structure & Algorithms 101 | Girliemac
- Trending in 3D Vision | Dragonlong
- Master Git list | Master Git & GitHub
- Algorithms / Data Structures | A Picture is worth a 1000 words
- Udacity's autonomous car | The Udacity open source self-driving car project
- Muhammad Luqman | Robotics Tutorials on YouTube by Muhammad
- Hummingbird | Life in Robotics and Technology
- ROS Tutorials by CLEARPATH | Step-by-step tutorials on ROS by Clearpath
- Robótica Educacional | Robótica Educacional Paraná
- Roboflow Notebooks | Jupyter Notebooks with Computer Vision tutorials
- Autonomous Driving Lecture | Autonomous driving lecture resources by Mayakshanesht
- C++ Best Practices | Collaborative Collection of C++ Best Practices
- C++ Optimizations Diary | Tips and tricks to optimize your C++ code
- Visual SLAM Roadmap | Roadmap to becoming a Visual-SLAM developer in 2022 by @changh95
- Official Repository RVSS | Colab notebooks for the Robotic Vision Summer School by @Tobias-Fischer
- KF in Python | Kalman Filter in Python
- Self Driving Cars | Coursera Open Courses from University of Toronto
- Coding Notes and Tutorials | A compilation of tutorials and references for different coding languages and frameworks
- Introduction to Robotics | Princeton University
- Michigan Robotics | Michigan Robotics courses
- Sensor Fusion Tutorial | A tutorial for sensor fusion using the robot_localization package
- Coding Notes | Coding notes in many programming languages by methylDragon
- Rosetta Launch | A guide to understanding launch files in ROS 1 and ROS 2
- Google Interview Preparation | Collection of problems and solutions of leetcode, geeksforgeeks challenges by mgechev