A Survey on Robot Navigation Based on Mammalian Spatial Cognition

被引:0
作者
Meng Z. [1 ]
Zhao D. [2 ]
Si B. [3 ]
Dai S. [1 ]
机构
[1] School of Mechanical Engineering, Hebei University of Technology, Tianjin
[2] Information Science Academy, China Electronics Technology Group Corporation, Beijing
[3] School of Systems Science, Beijing Normal University, Beijing
来源
Jiqiren/Robot | 2023年 / 45卷 / 04期
关键词
autonomous navigation; brain-inspired navigation; entorhinal cortex; hippocampus; spatial cognition;
D O I
10.13973/j.cnki.robot.220077
中图分类号
学科分类号
摘要
Hippocampus and entorhinal cortex of mammalian brains are key brain regions involved in spatial cognition, enabling animals to autonomously navigate in unknown environments. With the increasing demand on autonomous and intelligent navigation systems, it is extremely urgent to develop brain-inspired robot navigation technologies. In this paper, the neurophysiological mechanisms of spatial cognition of mammals are reviewed, and computational models of spatial cognition and their applications to brain-like robot navigation systems are introduced. Finally, the challenges and future research directions of brain-like navigation technology are discussed. © 2023 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码:496 / 512
页数:16
相关论文
共 124 条
  • [1] Durrant-Whyte H, Bailey T., Simultaneous localization and mapping: Part I, IEEE Robotics & Automation Magazine, 13, 2, pp. 99-110, (2006)
  • [2] Bailey T, Durrant-Whyte H., Simultaneous localization and mapping (SLAM): Part II, IEEE Robotics & Automation Magazine, 13, 3, pp. 108-117, (2006)
  • [3] Montemerlo M, Thrun S, Koller D, Et al., FastSLAM: A factored solution to the simultaneous localization and mapping problem, AAAI National Conference on Artificial Intelligence, pp. 593-598, (2002)
  • [4] Grisetti G, Tipaldi G D, Stachniss C, Et al., Fast and accurate SLAM with Rao-Blackwellized particle filters, Robotics and Autonomous Systems, 55, 1, pp. 30-38, (2007)
  • [5] Klein G, Murray D., Parallel tracking and mapping for small AR workspaces, 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225-234, (2007)
  • [6] Mur-Artal R, Montiel J M M, Tardios J D., ORB-SLAM: A versatile and accurate monocular SLAM system, IEEE Transactions on Robotics, 31, 5, pp. 1147-1163, (2015)
  • [7] Mur-Artal R, Tardos J D., ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras, IEEE Transactions on Robotics, 33, 5, pp. 1255-1262, (2017)
  • [8] Campos C, Elvira R, Rodriguez J J G, Et al., ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM, IEEE Transactions on Robotics, 37, 6, pp. 1874-1890, (2021)
  • [9] Strasdat H, Montiel J M M, Davison A J., Visual SLAM: Why filter?, Image and Vision Computing, 30, 2, pp. 65-77, (2012)
  • [10] Li R H, Wang S, Long Z Q, Et al., UnDeepVO: Monocular visual odometry through unsupervised deep learning, IEEE International Conference on Robotics and Automation, pp. 7286-7291, (2018)