Vision-based primary localization method for SLAM mobile robots

Mingcen Gu; Stephen Karungaru; Kenji Terada; Yuehua Li

doi:10.46947/joaasr632024944

Authors

Mingcen Gu Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan. School of Information Science and Technology, Nantong University, Nantong, China.
Stephen Karungaru Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan.
Kenji Terada Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan.
Yuehua Li School of Information Science and Technology, Nantong University, Nantong, China.

DOI:

https://doi.org/10.46947/joaasr632024944

Keywords:

SLAM, AMCL, ORB feature points, Initial pose, Identifying wall corners

Abstract

The AMCL (Adaptive Monte Carlo Localization) algorithm with visual provision of initial values is proposed to address the slow localization speed caused by conventional laser SLAM (Simultaneous Localization and Mapping) without initial poses and the global localization failure after a robot abduction event. In the initial map building phase, the ORB (Oriented FAST and Rotated BRIEF) feature values are extracted from the camera and the wall corners are identified, and then the pose information is stored in the database and a feature dictionary is constructed. After restarting, the dictionary is called to perform loopback detection by receiving the images captured by the current camera, and a successful detection results in a rough initial pose. If the detection fails, the initial pose is roughly calculated by identifying the wall corners. Finally, the particle filtering algorithm scatters particles in a small area near the obtained pose and converges to obtain a relatively accurate pose.

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References

Gaur A, Pawar M.AGV Based Material Handling System: A Literature Review [J].International Journal of Research; Scientific Innovation , Ill, 2016.

Zh. Y.C.The innovation path of AGV navigation technology [J]Logistics Technology and Applications,2018,v.23;No.221(7): 16-9

Berman S, Schechtman E, Edan Y. Evaluation Of Automatic Guided Vehicle Systems [J]. Robotics and Computer Integrated Manufacturing, 2009, 25(3): 522-8. DOI: https://doi.org/10.1016/j.rcim.2008.02.009

Vis I F A, Survey of research in the design and control of automated guided vehicle systems[J]. European Journal of Operational Research, 2004, 170(3): DOI: https://doi.org/10.1016/j.ejor.2004.09.020

Xue-Meng Yang, Min-Ru Yao, Kai Cao. A review of key problems and solutions of SLAM for mobile robots[J]. Computer System Applications,2018,27(7): 1-10

Leonard J J, Durrant-Whyte H F, Cox I J. Dynamic map building for an autonomous mobile robot[J]. International Journal of Robotics Research, 1992, 11(4): 286-298. DOI: https://doi.org/10.1177/027836499201100402

Smith R.C, Cheeseman P. On the representation and estimation of spatial uncertainty[J]. International Journal of Robotics Research,1987(5): 56-68. DOI: https://doi.org/10.1177/027836498600500404

Weininger D. Smiles I. Introduction and encoding rules[J]. Journal of Chemical Information and Computer Sciences,1988,28:31-36. DOI: https://doi.org/10.1021/ci00057a005

Smith R, Self M, Cheeseman P. Estimating uncertain spatial relationships in robotics[M].Autonomous robot vehicles. Springer New York, 1990: 435-461. DOI: https://doi.org/10.1007/978-1-4613-8997-2_14

Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of basic Engineering,1960,82(1): 35-45. DOI: https://doi.org/10.1115/1.3662552

Davison A J, Reid I D, Molton N D, et al. Mono SLAM: real-time single camera SLAMU[J].IEEE Trans Pattern Anal Mach Intell,2007,29(6):1052-1067. DOI: https://doi.org/10.1109/TPAMI.2007.1049

Davison A J. Real-Time Simultaneous Localisation and Mapping with a SingleCamera[C]. IEEE International Conference on Computer Vision. IEEE Computer Society.2003:1403. DOI: https://doi.org/10.1109/ICCV.2003.1238654

Doucet A, De Freitas N, Murphy K,et al. Rao-Blackwellised particle filtering for dynamic Bayesian networks[C]. Proceedings of the Sixteenth conference on Uncertainty inartificial intelligence. Morgan Kaufmann Publishers Inc., 2000: 176-183.

Montemerlo M, Thrun S, Koller D, et al. Fast SLAM: a factored solution to thesimultaneous localization and mapping problem[C]. Proc. of Theaaai National Conference on ArtificialIntelligence.2002:593-598

Grisetti G, Stachniss C, Burgard W. Improved techniques for grid mapping withrao-blackwellized particle filters [J]. IEEE Transactions on Robotics,2007,23(1): 34-46 DOI: https://doi.org/10.1109/TRO.2006.889486

Dong F., Liu Z, Kong D, et al. Adapting the sample size in particle filters through KLD-sampling[J]. International Journal of Robotics Research, 2003, 22(12): 985-1003. DOI: https://doi.org/10.1177/0278364903022012001

Klein G, Murray D. Parallel tracking and mapping for small AR workspaces[C]. Nara, Japan: IEEE Computer Society,2007:1-10. DOI: https://doi.org/10.1109/ISMAR.2007.4538852

Mur-Artal R, Montiel J M M, Tardos J D .ORB-SLAM: A Versatile and Accurate Monocular SLAM System[J]. Robotics EEE Transactions on,2015,31(5):1147-1163. DOI: https://doi.org/10.1109/TRO.2015.2463671

Mur-Artal R, Tardos J D.ORB-SLAM2: An Open-Source SLAM System for Monocular. Stereo, and RGB-D Cameras [J]. IEEE Transactions on Robotics,2017,33(5): 1255-1262. DOI: https://doi.org/10.1109/TRO.2017.2705103

EngelJ, Schops T, Cremers D. LSD-SLAM: Large-Scale Direct monocular SLAM[C] Zurich, Switzerland: Springer Verlag, 2014: 834-849. DOI: https://doi.org/10.1007/978-3-319-10605-2_54

Engel J, Sturm J, Cremers D. Semi-dense visual odometry for a monocular camera[C]Sydney, NSW, Australia: Institute of Electrical and Electronics Engineers Inc, 2014:1449-1456. DOI: https://doi.org/10.1109/ICCV.2013.183

Forster C , Pizzoli M ,Davide Scaramuzza. SVO: Fast Semi-Direct Monocular Visual Odometry[C]. EEE International Conference on Robotics & Automation. IEEE2014:15-22 DOI: https://doi.org/10.1109/ICRA.2014.6906584

Hess W, Kohler D, Rapp H, et al. Real-timeloop closure in 2D LIDAR SLAM [C]. EEE International Conference on Robotics and Automation,2016: 1271-1278. DOI: https://doi.org/10.1109/ICRA.2016.7487258

Rosten E, Drummond T. Machine learning for high-speed corner detection[C]. Graz. Austria: Springer Verlag,2006:430-443. DOI: https://doi.org/10.1007/11744023_34

Calonder M, Lepetit V, Strecha C, et al. BRIEF: Binary robust independent elementary features[C]. Springer Verlag,2010: 778-792. DOI: https://doi.org/10.1007/978-3-642-15561-1_56

Galvez-Lpez D , Tardos J D . Bags of Binary Words for Fast Place Recognition in Image Sequences[J]. Robotics, IEEE Transactions on,2012,28(5):1188-1197. DOI: https://doi.org/10.1109/TRO.2012.2197158

Lloyd S P . Least Squares Quantization in PCM's[J]. IEEE Transactions on Information Theory,1982,28(2):129-136. DOI: https://doi.org/10.1109/TIT.1982.1056489