Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps
Fusing 3-D maps generated by multiple robots in real/semi-real time distributed mapping systems are addressed in this paper. A 3-D occupancy grid-based approach for mapping is utilized to satisfy the real/semi-real time and distributed operating constraints. This paper proposes a novel hierarchical...
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sg-ntu-dr.10356-1063042019-12-06T22:08:37Z Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps Yue, Yufeng Senarathne, P. G. C. Namal Yang, Chule Zhang, Jun Wen, Mingxing Wang, Danwei School of Electrical and Electronic Engineering Sensors Probabilistic Logic Engineering::Electrical and electronic engineering Fusing 3-D maps generated by multiple robots in real/semi-real time distributed mapping systems are addressed in this paper. A 3-D occupancy grid-based approach for mapping is utilized to satisfy the real/semi-real time and distributed operating constraints. This paper proposes a novel hierarchical probabilistic fusion framework, which consists of uncertainty modeling, map matching, transformation evaluation, and map merging. Before the fusion of maps, the map features and their uncertainties are explicitly modeled and integrated. For map matching, a two-level probabilistic map matching (PMM) algorithm is developed to include high-level structural and low-level voxel features. In the PMM, the structural uncertainty is first used to generate a coarse matching between the maps and its result is then used to improve the voxel level map matching, resulting in a more efficient and accurate matching between maps with a larger convergence basin. The relative transformation output from PMM algorithm is then evaluated based on the Mahalanobis distance, and the relative entropy filter is used subsequently to integrate the map dissimilarities more accurately, completing the map fusion process. The proposed approach is evaluated using map data collected from both simulated and real environments, and the results validate the accuracy, efficiency, and the support for larger convergence basin of the proposed 3-D occupancy map fusion framework. Accepted version 2019-06-28T06:39:55Z 2019-12-06T22:08:37Z 2019-06-28T06:39:55Z 2019-12-06T22:08:37Z 2018 2018 Journal Article Yue, Y., Senarathne, P. G. C. N., Yang, C., Zhang, J., Wen, M., & Wang, D. (2018). Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps. IEEE Sensors Journal, 18(21), 8933-8949. doi:10.1109/JSEN.2018.2867854 1530-437X https://hdl.handle.net/10356/106304 http://hdl.handle.net/10220/49018 https://doi.org/10.1109/JSEN.2018.2867854 210572 en IEEE Sensors Journal © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JSEN.2018.2867854 17 p. application/pdf |
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Sensors Probabilistic Logic Engineering::Electrical and electronic engineering Yue, Yufeng Senarathne, P. G. C. Namal Yang, Chule Zhang, Jun Wen, Mingxing Wang, Danwei Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps |
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Fusing 3-D maps generated by multiple robots in real/semi-real time distributed mapping systems are addressed in this paper. A 3-D occupancy grid-based approach for mapping is utilized to satisfy the real/semi-real time and distributed operating constraints. This paper proposes a novel hierarchical probabilistic fusion framework, which consists of uncertainty modeling, map matching, transformation evaluation, and map merging. Before the fusion of maps, the map features and their uncertainties are explicitly modeled and integrated. For map matching, a two-level probabilistic map matching (PMM) algorithm is developed to include high-level structural and low-level voxel features. In the PMM, the structural uncertainty is first used to generate a coarse matching between the maps and its result is then used to improve the voxel level map matching, resulting in a more efficient and accurate matching between maps with a larger convergence basin. The relative transformation output from PMM algorithm is then evaluated based on the Mahalanobis distance, and the relative entropy filter is used subsequently to integrate the map dissimilarities more accurately, completing the map fusion process. The proposed approach is evaluated using map data collected from both simulated and real environments, and the results validate the accuracy, efficiency, and the support for larger convergence basin of the proposed 3-D occupancy map fusion framework. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Yue, Yufeng Senarathne, P. G. C. Namal Yang, Chule Zhang, Jun Wen, Mingxing Wang, Danwei |
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Article |
author |
Yue, Yufeng Senarathne, P. G. C. Namal Yang, Chule Zhang, Jun Wen, Mingxing Wang, Danwei |
author_sort |
Yue, Yufeng |
title |
Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps |
title_short |
Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps |
title_full |
Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps |
title_fullStr |
Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps |
title_full_unstemmed |
Hierarchical probabilistic fusion framework for matching and merging of 3-D occupancy maps |
title_sort |
hierarchical probabilistic fusion framework for matching and merging of 3-d occupancy maps |
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2019 |
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https://hdl.handle.net/10356/106304 http://hdl.handle.net/10220/49018 https://doi.org/10.1109/JSEN.2018.2867854 |
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1681034936933416960 |