Abstract
Inspired by the discoveries in neuroscience, the method of visual place recognition develops toward using multiple homogenous spatial scales. We present a novel multi-scale place recognition algorithm mimicking the rodent map with multi-scale, discrete and overlapped characteristics. This visual system that can perform place recognition in the aerial environment without any constraint. We present a parallel and multi-channel processing network that can recognize places with a spatial scale and combine the output from these parallel processing channels. This recognizing network can utilize a multi-scale matching that builds associations between robotic activity and places at different spatial scales. Using two aerial datasets, the results demonstrate universal improvements achieved with multi-scale recognition approach. A systematic series of flight simulation experiments are conducted for analyzing the effect on the recognition and localization performance of varying matching scales. Finally, we present insights of further work in robotic navigation.
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References
Cummins, M., Newman, P.: FAB-MAP: probabilistic localization and map** in the space of appearance. Int. J. Robot. Res. 27(6), 647–665 (2008)
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)
Ball, D., Heath, S., Wiles, J., Wyeth, G., Corke, P., Milford, M.: OpenRatSLAM: an open source brain-based SLAM system. Auton. Robot. 34(3), 149–176 (2013)
Milford, M., Wyeth, G.: Persistent navigation and map** using a biologically inspired SLAM system. Int. J. Robot. Res. 29(9), 1131–1153 (2010)
Milford, M., Wyeth, G.: SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: 2012 IEEE International Conference on Robotics and Automation, pp. 1643–1649. IEEE, Minnesota USA (2012)
Stensola, H., Stensola, T., Solstad, T., Frøland, K., Moser, M.B., Moser, E.I.: The entorhinal grid map is discretized. Nature 492(7427), 72–78 (2012)
Hafting, T., Fyhn, M., Molden, S., Moser, M.B., Moser, E.I.: Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052), 801–806 (2005)
Burak, Y., Fiete, I.R.: Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5(2), e1000291 (2009)
Chen, Z., Lowry, S., Jacobson, A., Hasselmo, M.E., Milford, M.: Bio-inspired homogeneous multi-scale place recognition. Neural Netw. 72, 48–61 (2015)
Ulanovsky, N., Moss, C.F.: Hippocampal cellular and network activity in freely moving echolocating bats. Nat. Neurosci. 10(2), 224–233 (2007)
Yartsev, M.M., Witter, M.P., Ulanovsky, N.: Grid cells without theta oscillations in the entorhinal cortex of bats. Nature 479(7371), 103–107 (2011)
Fan, C., Chen, Z., Jacobson, A., Hu, X., Milford, M.: Biologically-inspired visual place recognition with adaptive multiple scales. Robot. Auton. Syst. 96, 224–237 (2017)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: European Conference on Computer Vision, pp. 404–417. Springer (2006)
Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE, Minneapolis USA (2007)
Fan, C., Hu, X., Lian, J., Zhang, L., He, X.: Design and calibration of a novel camera-based bio-inspired polarization navigation sensor. IEEE Sens. J. 16(10), 3640–3648 (2016)
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This work was supported by the National Nature Science Foundation of China under Grant 61773394 and Grant 61573371, and Australian Research Council Future Fellowship FT140101229.
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Fan, C. et al. (2022). Bio-inspired Multi-scale Visual Place Recognition for the Aerial Vehicle Navigation. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_87
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DOI: https://doi.org/10.1007/978-981-15-8155-7_87
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