Awesome_Lifelong_SLAM

My personal list of important content related to Lifelong SLAM and Robotics.

Table of Contents

• Related survey papers
  
• Related research papers
  
• Datasets
  
• Laboratories and Research Groups
  
• Conferences
  
• Journals
  
• Courses
  

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Related survey papers:

• A Systematic Literature Review on Long-Term Localization and Mapping for Mobile Robots. RB Sousa. the University of Porto. 2022

• A survey: which features are required for dynamic visual simultaneous localization and mapping?. Zewen Xu,CAS. 2021

Related research papers

• (ISRR 2022)Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning

  • 弗莱堡大学的持续SLAM，可以快速适应新环境，记忆以前见过的环境信息。 page，code

• (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

• [2203.03923] ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping in Changing Environments IROS 2022

  • code：https://github.com/HaisenbergPeng/ROLL

• 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

• 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

• 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

• 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，

• 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

• 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。

• 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

• G. D. Tipaldi, D. Meyer-Delius, and W. Burgard, “Lifelong localization in changing environments,” IJRR 2013

  • life-long的定位

• 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

• 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

• 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

• 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。

• Pfreundschuh, Patrick, et al. “Dynamic Object Aware LiDAR SLAM Based on Automatic Generation of Training Data.” (ICRA 2021)

  • ETH ASL,code, video, dataset, Lidar
  • 作者基于deep-learning（3D-MiniNet网络）进行实时3D动态物体检测，滤除动态物体后的点云被喂给LOAM，进行常规的激光SLAM。提供了学习的方法是属于无监督的方法。

• Chen Xieyuanli, et al. “Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data.” IEEE Robotics and Automation Letters, 2021

  • code. video. University of Bonn.

• Rosen, David M., et al. “Towards Lifelong Feature-Based Mapping in Semi-Static Environments.” (ICRA 2016)

• Luiten Jonathon, et al. “Track to Reconstruct and Reconstruct to Track.”, (RAL+ICRA 2020)

  • code, video. Reconstruct.

• Grinvald, Margarita, et al. “TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction.”(ICRA 2021)

  • code, video.

Datasets

• 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

• 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

• 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

• 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

• 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

• 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

Laboratories and Research Groups

Global

• 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

Chinese

• 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

Conferences

• 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)

Journals

• 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

Courses

• 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