Bionic SLAM Algorithm Based on Interest Tendency Mechanism

CHEN Mengyuan; ZHANG Yukun; TIAN Dehong; DING Lingmei

doi:10.11999/JEIT210313

Volume 44 Issue 5

May 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1743-1753

CHEN Mengyuan, ZHANG Yukun, TIAN Dehong, DING Lingmei. Bionic SLAM Algorithm Based on Interest Tendency Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1743-1753. doi: 10.11999/JEIT210313

Citation:

CHEN Mengyuan, ZHANG Yukun, TIAN Dehong, DING Lingmei. Bionic SLAM Algorithm Based on Interest Tendency Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1743-1753. doi: 10.11999/JEIT210313

Citation:

PDF( 5795 KB)

Bionic SLAM Algorithm Based on Interest Tendency Mechanism

doi: 10.11999/JEIT210313

1.
School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
2.
Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Wuhu 241000, China

Funds: The National Natural Science Foundation of China (61903002), The Science and Technology Planning Project of Wuhu, Anhui Province (2020yf59), The Anhui Polytechnic University-Jiujiang District Industry Collaborative Innovation Special Foundation (2021cyxtb8), The Middle-aged and Top-notch Talent Project of Anhui Polytechnic University, The University Synergy Innovation Program of Anhui Province (GXXT-2021-050)

Received Date: 2021-04-15
Accepted Date: 2021-11-05
Rev Recd Date: 2021-06-24

Available Online: 2021-11-15

Publish Date: 2022-05-25

Abstract

Abstract

To address the problem that Simultaneous Location And Mapping (SLAM) closed-loop detection algorithms are easily disturbed by complex environmental factors, resulting in large localization errors and low closed-loop detection accuracy, a bionic SLAM algorithm based on the interest tendency mechanism is proposed, inspired by the spatial cognitive mechanism of mammals. The grid cells are modelled using the Lateral Anti-Hebbian Network (LAHN), which improves the accuracy of the algorithm by correcting the grid cells with irregular and complex environmental boundary information. The tendency of interest mechanism is used to score the extracted areas of significance, reduce the impact of redundant significant areas and improve the accuracy of the system’s closed-loop detection. A cognitive map is constructed by correlating the location information obtained from the location-aware model with a visual perception template. The results of the tests on the public dataset and the real environment show that the proposed algorithm has advantages in terms of accuracy, real time performance and adaptability to the environment.
- Simultaneous Localization And Mapping (SLAM),
- Cognitive map,
- Area of significance,
- Grid cells,
- Place cells

FullText(HTML)

References(23)

References

[1]	左燕, 周夏磊, 蒋陶然. 传感器位置误差下外辐射源雷达三维定位代数解算法[J]. 电子与信息学报, 2020, 42(3): 555–562. doi: 10.11999/JEIT190292 ZUO Yan, ZHOU Xialei, and JIANG Taoran. Algebraic solution for 3D localization of multistatic passive radar in the presence of sensor position errors[J]. Journal of Electronics &Information Technology, 2020, 42(3): 555–562. doi: 10.11999/JEIT190292
[2]	李世宝, 王升志, 刘建航, 等. 基于接收信号强度非齐性分布特征的半监督学习室内定位指纹库构建[J]. 电子与信息学报, 2019, 41(10): 2302–2309. doi: 10.11999/JEIT180599 LI Shibao, WANG Shengzhi, LIU Jianhang, et al. Semi-supervised indoor fingerprint database construction method based on the nonhomogeneous distribution characteristic of received signal strength[J]. Journal of Electronics &Information Technology, 2019, 41(10): 2302–2309. doi: 10.11999/JEIT180599
[3]	李庆华, 尤越, 沐雅琪, 等. 一种针对大型凹型障碍物的组合导航算法[J]. 电子与信息学报, 2020, 42(4): 917–923. doi: 10.11999/JEIT190179 LI Qinghua, YOU Yue, MU Yaqi, et al. Integrated navigation algorithm for large concave obstacles[J]. Journal of Electronics &Information Technology, 2020, 42(4): 917–923. doi: 10.11999/JEIT190179
[4]	MOSER E I, KROPFF E, and MOSER M B. Place cells, grid cells, and the brain's spatial representation system[J]. Annual Review of Neuroscience, 2008, 31(1): 69–89. doi: 10.1146/annurev.neuro.31.061307.090723
[5]	MOSER E I, ROUDI Y, WITTER M P, et al. Grid cells and cortical representation[J]. Nature Reviews Neuroscience, 2014, 15(7): 466–481. doi: 10.1038/nrn3766
[6]	YUAN Miaolong, TIAN Bo, SHIM V A, et al. An entorhinal-hippocampal model for simultaneous cognitive map building[C]. Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, USA, 2015: 586–592.
[7]	HAFTING T, FYHN M, MOLDEN S, et al. Microstructure of a spatial map in the entorhinal cortex[J]. Nature, 2005, 436(7052): 801–806. doi: 10.1038/nature03721
[8]	DOELLER C F, BARRY C, and BURGESS N. Evidence for grid cells in a human memory network[J]. Nature, 2010, 463(7281): 657–661. doi: 10.1038/nature08704
[9]	O'KEEFE J and DOSTROVSKY J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat[J]. Brain Research, 1971, 34(1): 171–175. doi: 10.1016/0006-8993(71)90358-1
[10]	SOLSTAD T, BOCCARA C N, KROPFF E, et al. Representation of geometric borders in the entorhinal cortex[J]. Science, 2008, 322(5909): 1865–1868. doi: 10.1126/science.1166466
[11]	KRUPIC J, BURGESS N, and O'KEEFE J. Neural representations of location composed of spatially periodic bands[J]. Science, 2012, 337(6096): 853–857. doi: 10.1126/science.1222403
[12]	BARRY C, HAYMAN R, BURGESS N, et al. Experience-dependent rescaling of entorhinal grids[J]. Nature Neuroscience, 2007, 10(6): 682–684. doi: 10.1038/nn1905
[13]	HARDCASTLE K, GANGULI S, and GIOCOMO L M. Environmental boundaries as an error correction mechanism for grid cells[J]. Neuron, 2015, 86(3): 827–839. doi: 10.1016/j.neuron.2015.03.039
[14]	JAYAKUMAR S, NARAYANAMURTHY R, RAMESH R, et al. Modeling the effect of environmental geometries on grid cell representations[J]. Frontiers in Neural Circuits, 2019, 12: 120. doi: 10.3389/fncir.2018.00120
[15]	REBAI K, AZOUAOUI O, and ACHOUR N. Bio-inspired visual memory for robot cognitive map building and scene recognition[C]. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, 2012: 2985–2990. doi: 10.1109/IROS.2012.6385493.
[16]	MILFORD M J and WYETH G F. Mapping a suburb with a single camera using a biologically inspired SLAM system[J]. IEEE Transactions on Robotics, 2008, 24(5): 1038–1053. doi: 10.1109/TRO.2008.2004520
[17]	GLOVER A J, MADDERN W P, MILFORD M J, et al. FAB-MAP + RatSLAM: Appearance-based SLAM for multiple times of day[C]. 2010 IEEE International Conference on Robotics and Automation, Anchorage, USA, 2010: 3507–3512. doi: 10.1109/ROBOT.2010.5509547.
[18]	HOU Yi, ZHANG Hong, and ZHOU Shilin. Convolutional neural network-based image representation for visual loop closure detection[C]. 2015 IEEE International Conference on Information and Automation, Lijiang, China, 2015: 2238–2245. doi: 10.1109/ICInfA.2015.7279659.
[19]	李维鹏, 张国良, 姚二亮, 等. 基于场景显著区域的改进闭环检测算法[J]. 机器人, 2017, 39(1): 23–35. doi: 10.13973/j.cnki.robot.2017.0023 LI Weipeng, ZHANG Guoliang, YAO Erliang, et al. An improved loop closure detection algorithm based on scene salient regions[J]. Robot, 2017, 39(1): 23–35. doi: 10.13973/j.cnki.robot.2017.0023
[20]	JUN H, BRAMIAN A, SOMA S, et al. Disrupted place cell remapping and impaired grid cells in a knockin model of alzheimer's disease[J]. Neuron, 2020, 107(6): 1095–1112. doi: 10.1016/j.neuron.2020.06.023
[21]	YU Shumei, WU Junyi, XU Haidong, et al. Robustness improvement of visual templates matching based on frequency-tuned model in RatSLAM[J]. Frontiers in Neurorobotics, 2020, 14: 568091. doi: 10.3389/fnbot.2020.568091
[22]	陈孟元, 徐明辉. 基于自组织可增长映射的移动机器人仿生定位算法研究[J]. 电子与信息学报, 2021, 43(4): 1003–1013. doi: 10.11999/JEIT200025 CHEN Mengyuan and XU Minghui. Research on mobile robot bionic location algorithm based on growing self-organizing map[J]. Journal of Electronics &Information Technology, 2021, 43(4): 1003–1013. doi: 10.11999/JEIT200025
[23]	陈孟元, 田德红. 基于多尺度网格细胞到位置细胞的仿生SLAM算法[J]. 计算机辅助设计与图形学学报, 2021, 33(5): 712–723. doi: 10.3724/SP.J.1089.2021.18407 CHEN Mengyuan and TIAN Dehong. Bionic SLAM algorithm based on multi-scale grid cell to place cell[J]. Journal of Computer-Aided Design &Computer Graphics, 2021, 33(5): 712–723. doi: 10.3724/SP.J.1089.2021.18407