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A Biologically-Inspired Simultaneous Localization and Mapping System Based on LiDAR Sensor (2109.12910v2)

Published 27 Sep 2021 in cs.RO

Abstract: Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM system based on a LiDAR sensor using a hippocampal model to build a cognitive map and estimate the robot pose in indoor environments. Based on the biologically inspired models mimicking boundary cells, place cells, and head direction cells, the SLAM system using LiDAR point cloud data is capable of leveraging the self-motion cues from the LiDAR odometry and the boundary cues from the LiDAR boundary cells to build a cognitive map and estimate the robot pose. Experiment results show that with the LiDAR boundary cells the proposed SLAM system greatly outperforms the camera-based brain-inspired method in both simulation and indoor environments, and is competitive with the conventional LiDAR-based SLAM methods.

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References (40)
  1. S. Thrun, “Probabilistic robotics,” Communications of the ACM, vol. 45, no. 3, pp. 52–57, 2002.
  2. T. Taketomi, H. Uchiyama, and S. Ikeda, “Visual slam algorithms: a survey from 2010 to 2016,” IPSJ Transactions on Computer Vision and Applications, vol. 9, no. 1, p. 16, 2017.
  3. J. M. Santos, D. Portugal, and R. P. Rocha, “An evaluation of 2d slam techniques available in robot operating system,” in 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).   IEEE, 2013, pp. 1–6.
  4. M. Filipenko and I. Afanasyev, “Comparison of various slam systems for mobile robot in an indoor environment,” in 2018 International Conference on Intelligent Systems (IS).   IEEE, 2018, pp. 400–407.
  5. S. Kohlbrecher, O. Von Stryk, J. Meyer, and U. Klingauf, “A flexible and scalable slam system with full 3d motion estimation,” in 2011 IEEE international symposium on safety, security, and rescue robotics.   IEEE, 2011, pp. 155–160.
  6. W. Hess, D. Kohler, H. Rapp, and D. Andor, “Real-time loop closure in 2d lidar slam,” in 2016 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2016, pp. 1271–1278.
  7. B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3d object detection from point clouds,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2018, pp. 7652–7660.
  8. C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 652–660.
  9. A. Asvadi, P. Girão, P. Peixoto, and U. Nunes, “3d object tracking using rgb and lidar data,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2016, pp. 1255–1260.
  10. E. I. Moser, E. Kropff, and M.-B. Moser, “Place cells, grid cells, and the brain’s spatial representation system,” Annu. Rev. Neurosci., vol. 31, pp. 69–89, 2008.
  11. J. O’Keefe and J. Dostrovsky, “The hippocampus as a spatial map: Preliminary evidence from unit activity in the freely-moving rat.” Brain research, 1971.
  12. J. O’Keefe, “Place units in the hippocampus of the freely moving rat,” Experimental neurology, vol. 51, no. 1, pp. 78–109, 1976.
  13. J. S. Taube, R. U. Muller, and J. B. Ranck, “Head-direction cells recorded from the postsubiculum in freely moving rats. i. description and quantitative analysis,” Journal of Neuroscience, vol. 10, no. 2, pp. 420–435, 1990.
  14. T. Hafting, M. Fyhn, S. Molden, M.-B. Moser, and E. I. Moser, “Microstructure of a spatial map in the entorhinal cortex,” Nature, vol. 436, no. 7052, pp. 801–806, 2005.
  15. M. E. Hasselmo, “Grid cell mechanisms and function: contributions of entorhinal persistent spiking and phase resetting,” Hippocampus, vol. 18, no. 12, pp. 1213–1229, 2008.
  16. T. Hartley, N. Burgess, C. Lever, F. Cacucci, and J. O’keefe, “Modeling place fields in terms of the cortical inputs to the hippocampus,” Hippocampus, vol. 10, no. 4, pp. 369–379, 2000.
  17. C. Lever, S. Burton, A. Jeewajee, J. O’Keefe, and N. Burgess, “Boundary vector cells in the subiculum of the hippocampal formation,” Journal of Neuroscience, vol. 29, no. 31, pp. 9771–9777, 2009.
  18. J. R. Hinman, G. W. Chapman, and M. E. Hasselmo, “Neuronal representation of environmental boundaries in egocentric coordinates,” Nature communications, vol. 10, no. 1, pp. 1–8, 2019.
  19. Z. Bing, A. E. Sewisy, G. Zhuang, F. Walter, F. O. Morin, K. Huang, and A. Knoll, “Toward cognitive navigation: Design and implementation of a biologically inspired head direction cell network,” IEEE Transactions on Neural Networks and Learning Systems, 2021.
  20. Z. Bing, C. Meschede, K. Huang, G. Chen, F. Rohrbein, M. Akl, and A. Knoll, “End to end learning of spiking neural network based on r-stdp for a lane keeping vehicle,” in 2018 IEEE international conference on robotics and automation (ICRA).   IEEE, 2018, pp. 4725–4732.
  21. M. Lechner, R. Hasani, A. Amini, T. A. Henzinger, D. Rus, and R. Grosu, “Neural circuit policies enabling auditable autonomy,” Nature Machine Intelligence, vol. 2, no. 10, pp. 642–652, 2020.
  22. Z. Bing, C. Meschede, G. Chen, A. Knoll, and K. Huang, “Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle,” Neural Networks, vol. 121, pp. 21–36, 2020.
  23. M. J. Milford, G. F. Wyeth, and D. Prasser, “Ratslam: a hippocampal model for simultaneous localization and mapping,” in IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, vol. 1.   IEEE, 2004, pp. 403–408.
  24. M. J. Milford and A. Jacobson, “Brain-inspired sensor fusion for navigating robots,” in 2013 IEEE International Conference on Robotics and Automation.   IEEE, 2013, pp. 2906–2913.
  25. S.-C. Zhou, R. Yan, J.-X. Li, Y.-K. Chen, and H. Tang, “A brain-inspired slam system based on orb features,” International Journal of Automation and Computing, vol. 14, no. 5, pp. 564–575, 2017.
  26. F. Yu, J. Shang, Y. Hu, and M. Milford, “Neuroslam: a brain-inspired slam system for 3d environments,” Biological Cybernetics, vol. 113, no. 5-6, pp. 515–545, 2019.
  27. M. J. Milford and G. F. Wyeth, “Mapping a suburb with a single camera using a biologically inspired slam system,” IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1038–1053, 2008.
  28. B. Tian, V. A. Shim, M. Yuan, C. Srinivasan, H. Tang, and H. Li, “Rgb-d based cognitive map building and navigation,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2013, pp. 1562–1567.
  29. M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain et al., “Loihi: A neuromorphic manycore processor with on-chip learning,” Ieee Micro, vol. 38, no. 1, pp. 82–99, 2018.
  30. S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, “The spinnaker project,” Proceedings of the IEEE, vol. 102, no. 5, pp. 652–665, 2014.
  31. R. Kreiser, A. Renner, Y. Sandamirskaya, and P. Pienroj, “Pose estimation and map formation with spiking neural networks: towards neuromorphic slam,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 2159–2166.
  32. R. Kreiser, M. Cartiglia, J. N. Martel, J. Conradt, and Y. Sandamirskaya, “A neuromorphic approach to path integration: a head-direction spiking neural network with vision-driven reset,” in 2018 IEEE international symposium on circuits and systems (ISCAS).   IEEE, 2018, pp. 1–5.
  33. G. Grisetti, C. Stachniss, and W. Burgard, “Improved techniques for grid mapping with rao-blackwellized particle filters,” IEEE transactions on Robotics, vol. 23, no. 1, pp. 34–46, 2007.
  34. J. Steckel and H. Peremans, “Batslam: Simultaneous localization and mapping using biomimetic sonar,” PloS one, vol. 8, no. 1, p. e54076, 2013.
  35. A. Elfes, “Using occupancy grids for mobile robot perception and navigation,” Computer, vol. 22, no. 6, pp. 46–57, 1989.
  36. C. Barry, C. Lever, R. Hayman, T. Hartley, S. Burton, J. O’Keefe, K. Jeffery, and N. Burgess, “The boundary vector cell model of place cell firing and spatial memory,” Reviews in the Neurosciences, vol. 17, no. 1-2, pp. 71–98, 2006.
  37. A. Samsonovich and B. L. McNaughton, “Path integration and cognitive mapping in a continuous attractor neural network model,” Journal of Neuroscience, vol. 17, no. 15, pp. 5900–5920, 1997.
  38. D. Ball, S. Heath, J. Wiles, G. Wyeth, P. Corke, and M. Milford, “Openratslam: an open source brain-based slam system,” Autonomous Robots, vol. 34, no. 3, pp. 149–176, 2013.
  39. M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng, “Ros: an open-source robot operating system,” in ICRA workshop on open source software, vol. 3, no. 3.2.   Kobe, 2009, p. 5.
  40. R. Kümmerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, and A. Kleiner, “On measuring the accuracy of slam algorithms,” Autonomous Robots, vol. 27, no. 4, pp. 387–407, 2009.
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Authors (5)
  1. Genghang Zhuang (5 papers)
  2. Zhenshan Bing (38 papers)
  3. Yuhong Huang (8 papers)
  4. Kai Huang (146 papers)
  5. Alois Knoll (189 papers)
Citations (5)
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