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slam_research_notes's Introduction

日志：

20220703-更新5月整理的ICRA2022的论文，以及终于把5月的整理结束了

SLAM_Research

@Author: zhuhu

@E-mail: [email protected]

@Github: zhuhu00

摘要

​ 整理前期主要以SLAM为主，包含论文，代码，相关讲解（如果有的话，一般在泡泡机器人slam公众号会发布），大概整理几个月会把激光SLAM也加上 ​ 目前收集的资料主要是一个大杂烩，研究的主要内容会从:tada:动态SLAM，Robust SLAM， Life-long，多传感器融合去跟进。将会从经典的框架开始，之后如果有时间的话，也会在这个仓库中添加文件夹写相关的基础。不一定能涵盖所有，请合理参考。

  1. 开源经典框架：经典、应用广泛的开源框架
  2. 优秀的作者和实验室：前期为大杂烩，后面则为主要关注的团队和个人，以及有意思的方向
  3. SLAM的学习资料：从基础的书籍到视频，数据集，公众号，代码注释等、
  4. 近期的论文：整理到的好论文，大概每月一次更新，前期会更新比较快。有关论文的笔记也许不会放出来了。

Attention：如果内容出现错误，请一定提出批评和建议，我一定及时修改。 于2021年4月正式启动（私密）。于2022年7月公开

首次更新于20210404，清明节的日子，开启这方面的学习跟踪， 灵感来自于Recent_SLAM_Research，Visual_SLAM_Related_Research

🎆以此Repository为基础，总结思路，学习优秀的方法，不能做到每篇文章都理解透彻，还得反复看，反复练习。

目录

  1. 经典代码框架
  2. 优秀作者及实验室
  3. 论文更新

0. 会议Paper List（SLAM）

1. 经典代码框架及实验室

见SLAM-approachs

2. 论文更新

2022年6月论文更新

2022年5月论文更新

2022年2月论文更新

  1. Autonomous Vehicles: Open-Source Technologies, Considerations, and Development, 有关自动驾驶车辆的开源框架,思考,及仿真环境等的综述, 2022
  2. A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence,2020, website ,深度学习在里程计, 定位, 建图, SLAM的综述

2021年11月论文更新

本月主要关注动态SLAM的整理和语义地图的论文，以及ICRA2022论文集也出来了，快速刷了一遍，把相关的放到了**ICRA2022.md**文件里了。

2021年11月论文更新

2021年10月论文更新

2021年9月论文更新

2021年8月论文更新

这部分论文更多在动态slam上，比7月更加focus。重点关注西班牙的博士所做的四篇文章的内容。以及在国内期刊上发表的一篇综述。

  • [1]Y. Liu and J. Miura, “RDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods,” IEEE Access, vol. 9, pp. 23772–23785, 2021, doi: 10.1109/ACCESS.2021.3050617.
    • 添加了语义及光流的信息
  • [2]Y. Liu and J. Miura, “RDMO-SLAM: Real-time Visual SLAM for Dynamic Environments using Semantic Label Prediction with Optical Flow,” IEEE Access, pp. 1–1, 2021, doi: 10.1109/ACCESS.2021.3100426.
    • 与上一篇文章框架类似。上一篇RDS-SLAM是这篇的拓展。
  • [3]F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller, and D. Cremers, “MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera,” arXiv:2011.11814 [cs], May 2021, Accessed: Aug. 10, 2021. [Online]. Available: http://arxiv.org/abs/2011.11814
    • CVPR上的单目重建框架，能去除动态物体
    • 开源
  • [4]J. Luiten, T. Fischer, and B. Leibe, “Track to Reconstruct and Reconstruct to Track,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 1803–1810, Apr. 2020, doi: 10.1109/LRA.2020.2969183.
    • RAL+ICRA2020的文章，重建中跟踪，跟踪重建。双目+光流+深度+mask
    • 开源
  • [5]B. Bescos, C. Campos, J. D. Tardós, and J. Neira, “DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5191–5198, Jul. 2021, doi: 10.1109/LRA.2021.3068640.
    • 大佬的又一篇很优秀的文章。不过暂时还没有开源
  • [6]T. Ran, L. Yuan, J. Zhang, D. Tang, and L. He, “RS-SLAM: A Robust Semantic SLAM in Dynamic Environments Based on RGB-D Sensor,” IEEE Sensors Journal, pp. 1–1, 2021, doi: 10.1109/JSEN.2021.3099511.
    • 先把图片分割后，再提取feature，进行tracking等的操作
  • [7]Z. Xu, Z. Rong, and Y. Wu, “A survey: which features are required for dynamic visual simultaneous localization and mapping?,” Vis. Comput. Ind. Biomed. Art, vol. 4, no. 1, p. 20, Dec. 2021, doi: 10.1186/s42492-021-00086-w.
    • 综述。主要是动态点在SLAM中到底有没有效果
  • [8]A. Kim, A. Ošep, and L. Leal-Taixé, “EagerMOT: 3D Multi-Object Tracking via Sensor Fusion,” arXiv:2104.14682 [cs], Apr. 2021, Accessed: Aug. 03, 2021. [Online]. Available: http://arxiv.org/abs/2104.14682
    • 开源，也是追踪重建类文章
  • [10]Y. Sun, M. Liu, and M. Q.-H. Meng, “Motion removal for reliable RGB-D SLAM in dynamic environments,” Robotics and Autonomous Systems,
  • [11]T. Qin, S. Cao, J. Pan, and S. Shen, “A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors,”
    • 大名鼎鼎的VINS-Fusion
  • [12]J. Cheng, Y. Sun, and M. Q.-H. Meng, “Robust Semantic Mapping in Challenging Environments,” Robotica, vol. 38, no. 2, pp. 256–270, Feb. 2020, doi: 10.1017/S0263574719000584.
  • [13]J. Zhang, M. Henein, R. Mahony, and V. Ila, “Robust Ego and Object 6-DoF Motion Estimation and Tracking,” arXiv:2007.13993 [cs], Jul. 2020, Accessed: Aug. 03, 2021. [Online]. Available: http://arxiv.org/abs/2007.13993
    • 使用mask，光流图像对动态物体进行估计。
  • [16]B. Bescos, J. Neira, R. Siegwart, and C. Cadena, “Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space,” arXiv:1809.10239 [cs, stat], Feb. 2019, Accessed: Aug. 02, 2021. [Online]. Available: http://arxiv.org/abs/1809.10239
  • [17]B. Bescos, C. Cadena, and J. Neira, “Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM,” IEEE Trans. Robot., vol. 37, no. 2, pp. 433–451, Apr. 2021, doi: 10.1109/TRO.2020.3031267.
  • [18]G. Li, X. Liao, H. Huang, S. Song, B. Liu, and Y. Zeng, “Robust Stereo Visual SLAM for Dynamic Environments With Moving Object,” IEEE Access, vol. 9, pp. 32310–32320, 2021, doi: 10.1109/ACCESS.2021.3059866.
  • [19]H. Xu, C. Yang, and Z. Li, “OD-SLAM: Real-Time Localization and Mapping in Dynamic Environment through Multi-Sensor Fusion,” in 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), Dec. 2020, pp. 172–177. doi: 10.1109/ICARM49381.2020.9195374.
  • [20]Y. Sun, M. Liu, and M. Q.-H. Meng, “Motion removal from moving platforms: An RGB-D data-based motion detection, tracking and segmentation approach,” in 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, Dec. 2015, pp. 1377–1382. doi: 10.1109/ROBIO.2015.7418963.
  • [21]Y. Liu, Y. Wu, and W. Pan, “Dynamic RGB-D SLAM Based on Static Probability and Observation Number,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2021, doi: 10.1109/TIM.2021.3089228.
  • [22]B. Bescos, J. M. Fácil, J. Civera, and J. Neira, “DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 4076–4083, Oct. 2018, doi: 10.1109/LRA.2018.2860039.
  • [23]D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context Encoders: Feature Learning by Inpainting,” arXiv:1604.07379 [cs], Nov. 2016, Accessed: Aug. 02, 2021. [Online]. Available: http://arxiv.org/abs/1604.07379
    • GAN补全图片
  • [24]P. Kaveti and H. Singh, “A Light Field Front-end for Robust SLAM in Dynamic Environments,” arXiv:2012.10714 [cs], Dec. 2020, Accessed: Aug. 01, 2021. [Online]. Available: http://arxiv.org/abs/2012.10714
    • 多相机去除动态物体
  • [25]J. Cheng, H. Zhang, and M. Q.-H. Meng, “Improving Visual Localization Accuracy in Dynamic Environments Based on Dynamic Region Removal,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1585–1596, Jul. 2020, doi: 10.1109/TASE.2020.2964938.
  • [26]J. C. V. Soares, M. Gattass, and M. A. Meggiolaro, “Crowd-SLAM: Visual SLAM Towards Crowded Environments using Object Detection,” J Intell Robot Syst, vol. 102, no. 2, p. 50, Jun. 2021, doi: 10.1007/s10846-021-01414-1.
    • 将人视为动态物体，去除
  • [27]X.-F. Han, S.-J. Sun, X.-Y. Song, and G.-Q. Xiao, “3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art,” arXiv:1802.02297 [cs], Jul. 2020, Accessed: Aug. 02, 2021. [Online]. Available: http://arxiv.org/abs/1802.02297
  • [28]C.-C. Wang, C. Thorpe, S. Thrun, M. Hebert, and H. Durrant-Whyte, “Simultaneous Localization, Mapping and Moving Object Tracking,” The International Journal of Robotics Research, vol. 26, no. 9, pp. 889–916, Sep. 2007, doi: 10.1177/0278364907081229.
  • [29]Kuen-Han Lin and Chieh-Chih Wang, “Stereo-based simultaneous localization, mapping and moving object tracking,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Oct. 2010, pp. 3975–3980. doi: 10.1109/IROS.2010.5649653.
  • [30]M. Ferrera, A. Eudes, J. Moras, M. Sanfourche, and G. Le Besnerais, “OV$^{2}$SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 1399–1406, Apr. 2021, doi: 10.1109/LRA.2021.3058069.
  • [31]D. M. Rosen, K. J. Doherty, A. T. Espinoza, and J. J. Leonard, “Advances in Inference and Representation for Simultaneous Localization and Mapping,” Mar. 2021, Accessed: Mar. 11, 2021. [Online]. Available: https://arxiv.org/abs/2103.05041v1
  • [32]J. Zhang, M. Henein, R. Mahony, and V. Ila, “VDO-SLAM: A Visual Dynamic Object-aware SLAM System,” arXiv:2005.11052 [cs], May 2020, Accessed: Jul. 28, 2021. [Online]. Available: http://arxiv.org/abs/2005.11052

2021年7月论文更新

本月主要关注动态SLAM及SLAM+MOT的内容。

  • [1]S. Chen, C.-W. Chang, and C.-Y. Wen, “Perception in the Dark; Development of a ToF Visual Inertial Odometry System,” Sensors, vol. 20, no. 5, p. 1263, Feb. 2020, doi: 10.3390/s20051263.

    • 还是之前那篇，TOF-VIO

  • [2]M. Mattamala, N. Chebrolu, and M. Fallon, “Ensuring Safe Visual Teach and Repeat Legged Navigation using Local World Representations,” p. 2.

  • [3]G. Kim and A. Kim, “Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018, pp. 4802–4809. doi: 10.1109/IROS.2018.8593953.

  • [4]W. Ali, P. Liu, R. Ying, and Z. Gong, “A life-long SLAM approach using adaptable local maps based on rasterized LIDAR images,” arXiv:2107.07133 [cs], Jul. 2021, Accessed: Jul. 21, 2021. [Online]. Available: http://arxiv.org/abs/2107.07133

  • [5]X. Qi, W. Wang, Z. Liao, X. Zhang, D. Yang, and R. Wei, “Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation,” Applied Sciences, vol. 10, no. 17, p. 5782, Aug. 2020, doi: 10.3390/app10175782.

  • [6]S. Chen, B. Zhou, C. Jiang, W. Xue, and Q. Li, “A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization,” Remote Sensing, vol. 13, no. 14, p. 2720, Jul. 2021, doi: 10.3390/rs13142720.

  • [7]H. Durrant-Whyte and T. Bailey, “Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms,” p. 9.

  • [8]M. Henein, “Meta Information in Graph-based Simultaneous Localisation And Mapping,” 2021, doi: 10.25911/7RJ5-XS18.

  • [9]J. Lee and S.-Y. Park, “PLF-VINS: Real-Time Monocular Visual-Inertial SLAM with Point-Line Fusion and Parallel-Line Fusion,” IEEE Robot. Autom. Lett., pp. 1–1, 2021, doi: 10.1109/LRA.2021.3095518.

  • [10]M. Yamaguchi, S. Mori, H. Saito, S. Yachida, and T. Shibata, “Global-Map-Registered Local Visual Odometry Using On-the-Fly Pose Graph Updates,” in Augmented Reality, Virtual Reality, and Computer Graphics, vol. 12242, L. T. De Paolis and P. Bourdot, Eds. Cham: Springer International Publishing, 2020, pp. 299–311. doi: 10.1007/978-3-030-58465-8_23.

  • [11]H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part I,” IEEE Robot. Automat. Mag., vol. 13, no. 2, pp. 99–110, Jun. 2006, doi: 10.1109/MRA.2006.1638022.

  • [12]T. Bailey and H. Durrant-Whyte, “Simultaneous localization and mapping (SLAM): part II,” IEEE Robot. Automat. Mag., vol. 13, no. 3, pp. 108–117, Sep. 2006, doi: 10.1109/MRA.2006.1678144.

  • [13]M. Jun, Z. Lilian, H. Xiaofeng, Q. Hao, and H. Xiaoping, “Precise Visual-Inertial Localization for UAV with the Aid of A 2D Georeferenced Map,” p. 8.

  • [14]S. Chen, C.-Y. Wen, Y. Zou, and W. Chen, “Stereo Visual Inertial Pose Estimation Based on Feedforward-Feedback Loops,” arXiv:2007.02250 [cs], Jul. 2020, Accessed: Jul. 13, 2021. [Online]. Available: http://arxiv.org/abs/2007.02250

  • [15]I. Hroob, R. Polvara, S. Molina, G. Cielniak, and M. Hanheide, “Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards,” arXiv:2107.05283 [cs], Jul. 2021, Accessed: Jul. 13, 2021. [Online]. Available: http://arxiv.org/abs/2107.05283

  • [16]X. Chen et al., “Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data,” IEEE Robot. Autom. Lett., pp. 1–1, 2021, doi: 10.1109/LRA.2021.3093567.

  • [17]G. Kim, A. Kim, and Y. Kim, “A new 3D space syntax metric based on 3D isovist capture in urban space using remote sensing technology,” Computers, Environment and Urban Systems, vol. 74, pp. 74–87, Mar. 2019, doi: 10.1016/j.compenvurbsys.2018.11.009.

  • [18]L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, N. Piatkowski, and C. Bauckhage, “Street-Map Based Validation of Semantic Segmentation in Autonomous Driving,” arXiv:2104.07538 [cs], Apr. 2021, Accessed: Jul. 12, 2021. [Online]. Available: http://arxiv.org/abs/2104.07538

  • [19]Z. Min and E. Dunn, “VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough,” arXiv:2104.06800 [cs], Apr. 2021, Accessed: Jul. 12, 2021. [Online]. Available: http://arxiv.org/abs/2104.06800

  • [20]J. C. Virgolino Soares, G. F. Abati, G. H. Duarte Lima, and M. A. Meggiolaro, “Autonomous Navigation System for a Wall-painting Robot based on Map Corners,” in 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), Natal, Brazil, Nov. 2020, pp. 1–6. doi: 10.1109/LARS/SBR/WRE51543.2020.9306998.

  • [21]M. Aygün et al., “4D Panoptic LiDAR Segmentation,” arXiv:2102.12472 [cs], Apr. 2021, Accessed: Jul. 12, 2021. [Online]. Available: http://arxiv.org/abs/2102.12472

  • [22]H. M. S. Bruno and E. L. Colombini, “LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method,” Neurocomputing, vol. 455, pp. 97–110, Sep. 2021, doi: 10.1016/j.neucom.2021.05.027.

  • [23]R. Bao, R. Komatsu, R. Miyagusuku, M. Chino, A. Yamashita, and H. Asama, “Stereo Camera Visual SLAM with Hierarchical Masking and Motion-state Classification at Outdoor Construction Sites Containing Large Dynamic Objects,” Advanced Robotics, vol. 35, no. 3–4, pp. 228–241, Feb. 2021, doi: 10.1080/01691864.2020.1869586.

  • [24]S. Cao, X. Lu, and S. Shen, “GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation,” arXiv:2103.07899 [cs], Apr. 2021, Accessed: Jul. 12, 2021. [Online]. Available: http://arxiv.org/abs/2103.07899

  • [25]H. Durrant‐Whyte, “Where am I? A tutorial on mobile vehicle localization,” Industrial Robot: An International Journal, vol. 21, no. 2, pp. 11–16, Jan. 1994, doi: 10.1108/EUM0000000004145.

  • [26]T. Ort, I. Gilitschenski, and D. Rus, “GROUNDED: The Localizing Ground Penetrating Radar Evaluation Dataset,” presented at the Robotics: Science and Systems 2021, Jul. 2021. doi: 10.15607/RSS.2021.XVII.080.

  • [27]A. G. Toudeshki, F. Shamshirdar, and R. Vaughan, “UAV Visual Teach and Repeat Using Only Semantic Object Features,” arXiv:1801.07899 [cs], Jan. 2018, Accessed: Jul. 07, 2021. [Online]. Available: http://arxiv.org/abs/1801.07899

  • [28]T. Krajník, P. Cristóforis, K. Kusumam, P. Neubert, and T. Duckett, “Image features for visual teach-and-repeat navigation in changing environments,” Robotics and Autonomous Systems, vol. 88, pp. 127–141, Feb. 2017, doi: 10.1016/j.robot.2016.11.011.

  • [29]L. Bertoni, S. Kreiss, T. Mordan, and A. Alahi, “MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization,” arXiv:2008.10913 [cs], Mar. 2021, Accessed: Jul. 07, 2021. [Online]. Available: http://arxiv.org/abs/2008.10913

  • [30]P. Yin, L. Xu, J. Zhang, H. Choset, and S. Scherer, “i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions,” presented at the Robotics: Science and Systems 2021, Jul. 2021. doi: 10.15607/RSS.2021.XVII.027.

  • [31]S. Schubert, P. Neubert, and P. Protzel, “Fast and Memory Efficient Graph Optimization via ICM for Visual Place Recognition,” presented at the Robotics: Science and Systems 2021, Jul. 2021. doi: 10.15607/RSS.2021.XVII.091.

    • RSS2021，用于视觉场景重定位

2021年6月论文更新

更新了11篇SLAM系统相关论文,VT&R的6篇论文,SLAM的综述或者benchmark论文2篇,其他5篇,其中有四篇来自CVPR 2021

SLAM系统

VT&R方面的论文更新

  • [1]P. Furgale and T. Barfoot, “Stereo mapping and localization for long-range path following on rough terrain,” in 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, May 2010, pp. 4410–4416. doi: 10.1109/ROBOT.2010.5509133.

    [2]P. Furgale and T. Barfoot, “Visual path following on a manifold in unstructured three-dimensional terrain,” in 2010 IEEE International Conference on Robotics and Automation, May 2010, pp. 534–539. doi: 10.1109/ROBOT.2010.5509140.

    [3]P. Furgale and T. D. Barfoot, “Visual teach and repeat for long-range rover autonomy: Furgale & Barfoot: Visual Teach and Repeat for Long-Range Rover Autonomy,” J. Field Robotics, vol. 27, no. 5, pp. 534–560, Sep. 2010, doi: 10.1002/rob.20342.

    • VT&R的最开始的文章，使用双目相机作为传感器进行long-range的导航问题。
    • 加拿大宇航局，数据集

  • [4]L. Sun et al., “Robust and Long-term Monocular Teach and Repeat Navigation using a Single-experience Map,” 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), p. 9.

    • 深度学习的Descriptor
    • 开源

  • [5]M. Mattamala, M. Ramezani, M. Camurri, and M. Fallon, “Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat,” arXiv:2103.14070 [cs], Mar. 2021, Accessed: Jul. 07, 2021. [Online]. Available: http://arxiv.org/abs/2103.14070

    • ICRA2021, 牛津的四足，作为主动slam的其中一个方面，在给定路径的同时，能够安全进行VT&R的工作，
    • 在仿真和实际中都进行了测试，同时在ICRA 2021的workshop中进行了介绍这种方法的安全性。

  • [6]D. Dall’Osto, T. Fischer, and M. Milford, “Fast and Robust Bio-inspired Teach and Repeat Navigation,” arXiv:2010.11326 [cs], Mar. 2021, Accessed: Jul. 07, 2021.

    • 新的思路去进行VT&R的工作

SLAM综述：

  • [1]G. Huang, “Visual-Inertial Navigation: A Concise Review,” arXiv:1906.02650 [cs], Jun. 2019, Accessed: Nov. 18, 2020. [Online]. Available: http://arxiv.org/abs/1906.02650
    • Guoquan Huang
    • VIN的综述

SLAM Benchmark

  • [1]M. Bujanca et al., “SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding,” in 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, May 2019, pp. 6351–6358. doi: 10/ghqdqd.
    • 对动态SLAM和环境理解（类似语义分割）的slam的Benchmark
    • 开源

其他

  • [0]A. Jelinek, A. Ligocki, and L. Zalud, “Robotic Template Library,” arXiv:2107.00324 [cs], Jul. 2021, Accessed: Jul. 03, 2021. [Online]. Available: http://arxiv.org/abs/2107.00324
  • [2]Q. Zhou, T. Sattler, M. Pollefeys, and L. Leal-Taixé, “To Learn or Not to Learn: Visual Localization from Essential Matrices,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 3319–3326. doi: 10/gh9smf.
    • 视觉定位，基于学习或者基于传统方法的比较，提出了新的方法
  • J. Li and G. H. Lee, “DeepI2P: Image-to-Point Cloud Registration via Deep Classification,” arXiv:2104.03501 [cs], Apr. 2021, Accessed: Jul. 29, 2021.CVPR
    • 点云和图像的配准
  • M. Geppert, V. Larsson, P. Speciale, J. L. Schonberger, and M. Pollefeys, “Privacy Preserving Localization and Mapping From Uncalibrated Cameras,” p. 11.
    • ETH做的关于云端SLAM的隐私保护问题，出现了隐私保护的文章，主要针对需要云服务的一些SLAM应用，目前已经做了几篇文章
    • 还未开源，目前这部分还不知道能做啥，
  • P. Karkus, S. Cai, and D. Hsu, “Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation,” arXiv:2105.07593 [cs, stat], May 2021, Accessed: Jul. 29, 2021. [Online]. Available: http://arxiv.org/abs/2105.07593，CVPR
  • S. H. Lee and J. Civera, “Rotation-Only Bundle Adjustment,” arXiv:2011.11724 [cs], Mar. 2021, Accessed: Jul. 29, 2021. [Online]. Available: http://arxiv.org/abs/2011.11724
    • MATLAB的代码
    • 对旋转的BA

2021年5月论文更新

此次更新了17篇论文

  • [1]J. Delmerico and D. Scaramuzza, “A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), May 2018, pp. 2502–2509. doi: 10/gf74jx.
    • 针对无人机上的power，计算能力，成本问题等，选择了camera和IMU进行状态估计，而没有外界的定位信息。比较累VIO的：MSCKF，OKVIS，ROVIO，VINS-Mono，SVO+MSF，SVO+GTSAM，最终在EuRoC数据集上考虑了位姿估计精度，每帧处理时长，CPU和内存的使用。同时也在4种平台上分别进行了测试。
    • ETH和UZH
  • [2]B. Huang, J. Zhao, and J. Liu, “A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks,” arXiv:1909.05214 [cs], Feb. 2020, Accessed: Jun. 15, 2021. [Online]. Available: http://arxiv.org/abs/1909.05214
    • 针对visual和lidar的slam系统进行了综述，介绍了很多个slam系统的特点。VIO，语义，多传感器融合，深度学习在slam中的一些论文，以及水下slam，Camera和IMU标定的算法等。
    • 黄百川，去深圳当计算机竞赛老师
  • [3]R. Milijas, L. Markovic, A. Ivanovic, F. Petric, and S. Bogdan, “A comparison of LiDAR-based SLAM systems for Control of Unmanned Aerial Vehicles,” arXiv:2011.02306 [cs], Mar. 2021, Accessed: Jun. 17, 2021. [Online]. Available: http://arxiv.org/abs/2011.02306
    • 使用激光SLAM对无人机的姿态进行估计，实现反馈控制。对比了loam，HDL graph slam，Cartographer三种算法。在阶跃响应，螺旋轨迹等上面进行了反馈控制和比较。文章将激光SLAM的输出作为无人机的pose sensor，结合IMU，通过DKF（Discrete Kalman Filter）进行预测和控制。
    • 未开源
  • [4]X. Zuo, N. Merrill, W. Li, Y. Liu, M. Pollefeys, and G. Huang, “CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth,” Dec. 2020. Accessed: Jun. 05, 2021. [Online]. Available: http://arxiv.org/abs/2012.10133
    • 设计了一种带有实时稠密重建的VIO系统，提出了一种新颖的CVAE神经网络用来估计深度并对深度进行编码，充分利用了VIO稀疏深度图的信息，并且拥有很强的泛化能力。
    • 没读懂。。。以后有机会再看看。ICRA2021 best student paper的候选之一。
    • 左星星，浙大，ETH，Guoquan Huang的RPNG组，好像未开源
  • [5]X. Gu, W. Yuan, Z. Dai, C. Tang, S. Zhu, and P. Tan, “DRO: Deep Recurrent Optimizer for Structure-from-Motion,” arXiv:2103.13201 [cs], May 2021, Accessed: Jun. 11, 2021. [Online]. Available: http://arxiv.org/abs/2103.13201
    • 提出zero-order的循环神经网络优化器（DRO）, 不需要求解梯度, 直接利用神经网络来预测下次更新的方向和步长。将优化目标cost，放入到神经网络中，每次迭代都会参考之前尝试的历史信息，从而给出更加精准的预测。
    • 阿里云实验室，谭平教授，开源，
  • [6]K. D. Katyal, A. Polevoy, J. Moore, C. Knuth, and K. M. Popek, “High-Speed Robot Navigation using Predicted Occupancy Maps,” arXiv:2012.12142v1[cs.RO], p. 7.
    • 一种使用栅格地图的导航算法，由于计算瓶颈和小的FOV，并且使用了GNN从现实世界中训练了一个模型，预测出栅格地图，拓展了控制算法，使汽车在未知区域内的高速规划和导航。其在实验中使用了D435i和T265作为其的传感器，然后建出来栅格地图。
    • ICRA2021，约翰霍普金斯大学。
  • [7]R. Voges and B. Wagner, “Interval-Based Visual-LiDAR Sensor Fusion,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1304–1311, Apr. 2021, doi: 10/gkdw6q.
    • 多传感器融合的方法，主要针对视觉和激光的融合。在视觉特征以点作为信息时，与激光的深度信息的不确定性是未知的，并且这种不确定性无法测量，另一个是针对坐标系的标定等出现的误差。解决方法是将视觉和激光的信息进行分区（块）处理，使得误差限定在一个区间内。使用融合后的信息进行了航位推算。保证了机器人的真实姿态。（略读）之后实现了一个里程计。
  • [8]K. Eckenhoff, P. Geneva, and G. Huang, “MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System,” IEEE Transactions on Robotics, pp. 1–21, 2021, doi: 10/gkdxp9.
    • 多IMU和多相机的VINS系统。
    • 黄国权教授
  • [9]M. Müller and V. Koltun, “OpenBot: Turning Smartphones into Robots,” arXiv:2008.10631 [cs], Mar. 2021, Accessed: Jun. 12, 2021. [Online]. Available: http://arxiv.org/abs/2008.10631
    • 使用手机作为计算平台的低成本小车，可实现跟随，识别，导航等功能。确实是便宜
    • 开源，复现ing，做一个出来玩玩
  • [10]B. Forkel, J. Kallwies, and H.-J. Wuensche, “Probabilistic Terrain Estimation for Autonomous Off-Road Driving,” p. 7.
    • 建高程图进行导航，将障碍物分离出来
  • [11]Z. Li et al., “Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers,” arXiv:2011.02910 [cs], Mar. 2021, Accessed: May 25, 2021. [Online]. Available: http://arxiv.org/abs/2011.02910
    • 利用transformers从seq2seq中进行双目深度估计
    • 开源
  • [12]M. Labbé and F. Michaud, “RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation,” Journal of Field Robotics, vol. 36, no. 2, pp. 416–446, 2019, doi: 10/gf3bgd.
    • RTab Map, 多传感器融合的一个好的框架，论文综述了许多SLAM算法的优缺点。还没读完
    • Introlab，开源
  • [13]J. Jeon, S. Jung, E. Lee, D. Choi, and H. Myung, “Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5332–5339, Jul. 2021, doi: 10/gkpqx8.
    • 在不同的Nvidia Jason板子上测试了很多的VI/VIO系统，主要包括three mono-VIO(VINS-Mono, ROVIO, and ALVIO), two stereoVO ORB-SLAM2 stereo and VINS-Fusion w/o IMU), and four stereo-VIO (VINS-Fusion w/IMU, VINS-Fusion w/GPU, Stereo-MSCKF, and Kimera) lgorithms and benchmark them on NVIDIA Jetson TX2, Xavier NX, and AGX Xavier boards. 同时自己建立了一个**数据集**，包扩circle, infinity, square, and pure_rotation 这几种轨迹或者姿态。
    • Benchmark + dataset，韩国KAIST
  • [14]H. Azpurua, M. F. M. Campos, and D. G. Macharet, “Three-dimensional Terrain Aware Autonomous Exploration for Subterranean and Confined Spaces,” p. 7.
    • 火星车可通过性研究，使用激光点云生成类似高程图，生成可通过性的地图。
    • Computer Vision and Robotics Laboratory (VeRLab)
  • [15]Z. Qian, K. Patath, J. Fu, and J. Xiao, “Semantic SLAM with Autonomous Object-Level Data Association,” arXiv:2011.10625 [cs], Nov. 2020, Accessed: May 31, 2021. [Online]. Available: http://arxiv.org/abs/2011.10625
    • 使用YOLO识别出物体（转化为词袋模型），然后对物体进行标记，最后使用曲面模型求物体进行标注。感觉语义性不是很强。
    • Worcester Polytechnic Institute
  • [16]Y. Latif, A.-D. Doan, T.-J. Chin, and I. Reid, “SPRINT: Subgraph Place Recognition for INtelligent Transportation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 5408–5414. doi: 10/gjn63n.
    • Place Recognition.场景重现识别（针对场景的不同时间段，相机拍摄的照片会不一样）
    • Visual的场景重识别，使用KNN的图像检索+图像采集过程中的时间关系，可以将匹配的位置限制在目前为止看到的场景的子集内。根据图像采集的关系，使用HMM进行缩小子集。
  • [17]D. Wisth, M. Camurri, S. Das, and M. Fallon, “Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 1004–1011, Apr. 2021, doi: 10/gjjm8s.
    • 多传感器融合，VI，slam。紧耦合的里程计。

2021年4月论文更新

此次更新21篇论文

  • [0] Fond, Antoine et al. “[End-to-end learning of keypoint detection and matching for relative pose estimation](End-to-end learning of keypoint detection and matching for relative pose estimation).” (2021).

    • 相对位置估计的关键点检测和匹配的端到端学习
    • arxiv上, 暂时无代码

  • [1]D. Geromichalos, M. Azkarate, E. Tsardoulias, L. Gerdes, L. Petrou, and C. P. D. Pulgar, “SLAM for autonomous planetary rovers with global localization,” Journal of Field Robotics, vol. 37, no. 5, pp. 830–847, 2020, doi: 10/ggrq3v.

    • 适用于全局定位的自主行星漫游车的SLAM
    • 使用粒子滤波器和扫描匹配，研究目的是为了支持远距离、低监督行星探索。
    • 火星车等的SLAM，地图的可通过性

  • [2]M. Azkarate, L. Gerdes, L. Joudrier, and C. J. Pérez-del-Pulgar, “A GNC Architecture for Planetary Rovers with Autonomous Navigation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 3003–3009. doi: 10/gjxsmn.

    • 针对火星车的GNC（Guidance，Navigation，Control）设计的一种系统，
    • 欧洲宇航局

  • [3]Y. Zhao and P. A. Vela, “Good Feature Selection for Least Squares Pose Optimization in VO/VSLAM,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018, pp. 1183–1189. doi: 10/ghs5cg.

    • 对特征选择的策略，使用对位姿估计最有帮助的特征。还提出另一种解决NP-hard Max-logDet的算法
    • 佐治亚理工学院Yipuzhao，作者有很多关于“Good”的文章

  • [4]A. J. Ben Ali, Z. S. Hashemifar, and K. Dantu, “Edge-SLAM: edge-assisted visual simultaneous localization and mapping,” in Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, New York, NY, USA, Jun. 2020, pp. 325–337. doi: 10/ghdxk5.

    • 边缘辅助的VSLAM，将系统进行分离，能够将一部分进程分析到其他设备中运行。
    • 开源
    • 纽约布法罗大学

  • [5]Z. Hashemifar and K. Dantu, “Practical Persistence Reasoning in Visual SLAM,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, May 2020, pp. 7307–7313. doi: 10/gjww5h.

    • 视觉SLAM中实用且持续的推理

  • [6]R. F. Salas-Moreno, R. A. Newcombe, H. Strasdat, P. H. J. Kelly, and A. J. Davison, “SLAM++: Simultaneous Localisation and Mapping at the Level of Objects,” in 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, Jun. 2013, pp. 1352–1359. doi: 10/ggmwnd.

    • 基于对象级的SLAM
    • 在建图过程中将3维物体重建出来

  • [7]Y. Zhao, J. S. Smith, S. H. Karumanchi, and P. A. Vela, “Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 1105–1112. doi: 10/gjv95x.

  • [8]Y. Fan, R. Wang, and Y. Mao, “Stereo Visual Inertial Odometry with Online Baseline Calibration,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 1084–1090. doi: 10/gjn63d.

    • 一种针对双目相机的在线标定视觉惯性里程计
    • 美团-点评Group

  • [9]P. Gao and H. Zhang, “Long-term Place Recognition through Worst-case Graph Matching to Integrate Landmark Appearances and Spatial Relationships,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 1070–1076. doi: 10/gjv2qx.

    • 位置识别，用于同一个场景中的地标会发生变化等的情况，

  • [10]M. Henein, J. Zhang, R. Mahony, and V. Ila, “Dynamic SLAM: The Need For Speed,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 2123–2129. doi: 10/gjv2md.

    • Dynamic SLAM：对速度的需求，对场景中运动的物体进行实时速度预测
    • 澳大利亚国立大学

  • [11]K. Wang, K. Wang, and S. Shen, “FlowNorm: A Learning-based Method for Increasing Convergence Range of Direct Alignment,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, May 2020, pp. 2109–2115. doi: 10/gjv2j2.

    • 港科

  • [12]B. Li, D. Zou, D. Sartori, L. Pei, and W. Yu, “TextSLAM: Visual SLAM with Planar Text Features,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 2102–2108. doi: 10/gjv2jf.

    • TextSLAM: 具有平面文本特征的VSLAM
    • 上交导航定位实验室
    • 识别环境中的文字信息的SLAM系统，是否算语义呢？（未开源）使用文字的平面信息

  • [13]H. Wang, C. Wang, and L. Xie, “Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 2095–2101. doi: 10/gjn69q.

    • Intensity Scan Context: 用于回环检测的编码强度和几何关系，使用激光的强度投影，进行回环的检测
    • 南洋理工大学，RI in CMU

  • [14]C. Schulz and A. Zell, “Real-Time Graph-Based SLAM with Occupancy Normal Distributions Transforms,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 3106–3111. doi: 10/gjn65h.

    • 具有正态分布变换的实时图SLAM。根据正态分布变化改进了Cartographer，设计了一个新的回环系统。

    • 德国杜宾根大学

  • [15]R. Nakashima and A. Seki, “Uncertainty-Based Adaptive Sensor Fusion for Visual-Inertial Odometry under Various Motion Characteristics,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 3119–3125. doi: 10/gjn6z5.

    • 不同运动特性下基于不确定度的视觉惯性里程计自适应传感器融合，自适应的使用状态的不确定性
    • 日本东芝

  • [16]J. Lin and F. Zhang, “Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 3126–3131. doi: 10/gjv2hr.

    • Loam_livox: 将loam集成到livox激光雷达中
    • 港大MARS实验室，专门做livox相关的。
    • 开源

  • [17]S. Zhao, H. Zhang, P. Wang, L. Nogueira, and S. Scherer, “Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments,” arXiv:2104.14938 [cs], Apr. 2021, Accessed: May 04, 2021. [Online]. Available: http://arxiv.org/abs/2104.14938

    • Super Odometry：以IMU为中心的LiDAR视觉惯性估计器，可用于具有挑战性的环境，弱光，浓雾等
    • DARPA挑战赛的一部分工作
    • CMU，东北大学

  • [18]P. Yin et al., “Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018, pp. 1162–1167. doi: 10/gjn63m.

    • 稳定的无监督特征学习在激光位置识别中的应用
    • CMU，沈阳自动化所

  • [19]P. Yin, L. Xu, J. Zhang, and H. Choset, “FusionVLAD: A Multi-View Deep Fusion Networks for Viewpoint-Free 3D Place Recognition,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2304–2310, Apr. 2021, doi: 10/gjn7bs.

    • FusionVLAD：用于无视点3d位置的多视图深度融合网络

  • [20]C. Won, H. Seok, Z. Cui, M. Pollefeys, and J. Lim, “OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 559–566. doi: 10/gh5kp8.

    • OmniSLAM：宽基线多相机系统的全向定位和密集映射

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