Camera Ego-Positioning Using Sensor Fusion and Complementary Method

Peng Yuan Kao; Kuan Wei Tseng; Tian Yi Shen; Yan Bin Song; Kuan Wen Chen; Shih Wei Hu; Sheng Wen Shih; Yi Ping Hung

doi:10.1007/978-3-030-68796-0_21

Camera Ego-Positioning Using Sensor Fusion and Complementary Method

Peng Yuan Kao, Kuan Wei Tseng, Tian Yi Shen, Yan Bin Song, Kuan Wen Chen, Shih Wei Hu, Sheng Wen Shih, Yi Ping Hung

Center for General Education

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Visual simultaneous localization and mapping (SLAM) is a common solution for camera ego-positioning. However, SLAM sometimes loses tracking, for instance due to fast camera motion or featureless or repetitive environments. To account for the limitations of visual SLAM, we use sensor fusion method to fuse the visual positioning results with inertial measurement unit (IMU) data based on filter-based, loosely-coupled sensor fusion methods, and further combines feature-based SLAM with direct SLAM via proposed complementary fusion to retain the advantages of both methods; i.e., we not only keep the accurate positioning of feature-based SLAM but also account for its difficulty with featureless scenes by direct SLAM. Experimental results show that the proposed complementary method improves the positioning accuracy of conventional vision-only SLAM and leads to more robust positioning results.

Original language	English
Title of host publication	Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
Editors	Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	291-304
Number of pages	14
ISBN (Print)	9783030687953
DOIs	https://doi.org/10.1007/978-3-030-68796-0_21
Publication status	Published - 2021
Event	25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Milan, Italy Duration: Jan 10 2021 → Jan 11 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12663 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Pattern Recognition Workshops, ICPR 2020
Country/Territory	Italy
City	Milan
Period	1/10/21 → 1/11/21

Keywords

Camera ego-positioning
Sensor fusion

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-68796-0_21

Cite this

Kao, P. Y., Tseng, K. W., Shen, T. Y., Song, Y. B., Chen, K. W., Hu, S. W., Shih, S. W., & Hung, Y. P. (2021). Camera Ego-Positioning Using Sensor Fusion and Complementary Method. In A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. M. Farinella, T. Mei, M. Bertini, H. J. Escalante, & R. Vezzani (Eds.), Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings (pp. 291-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12663 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-68796-0_21

Camera Ego-Positioning Using Sensor Fusion and Complementary Method. / Kao, Peng Yuan; Tseng, Kuan Wei; Shen, Tian Yi et al.
Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. ed. / Alberto Del Bimbo; Rita Cucchiara; Stan Sclaroff; Giovanni Maria Farinella; Tao Mei; Marco Bertini; Hugo Jair Escalante; Roberto Vezzani. Springer Science and Business Media Deutschland GmbH, 2021. p. 291-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12663 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kao, PY, Tseng, KW, Shen, TY, Song, YB, Chen, KW, Hu, SW, Shih, SW & Hung, YP 2021, Camera Ego-Positioning Using Sensor Fusion and Complementary Method. in A Del Bimbo, R Cucchiara, S Sclaroff, GM Farinella, T Mei, M Bertini, HJ Escalante & R Vezzani (eds), Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12663 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 291-304, 25th International Conference on Pattern Recognition Workshops, ICPR 2020, Milan, Italy, 1/10/21. https://doi.org/10.1007/978-3-030-68796-0_21

Kao PY, Tseng KW, Shen TY, Song YB, Chen KW, Hu SW et al. Camera Ego-Positioning Using Sensor Fusion and Complementary Method. In Del Bimbo A, Cucchiara R, Sclaroff S, Farinella GM, Mei T, Bertini M, Escalante HJ, Vezzani R, editors, Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 291-304. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-68796-0_21

Kao, Peng Yuan ; Tseng, Kuan Wei ; Shen, Tian Yi et al. / Camera Ego-Positioning Using Sensor Fusion and Complementary Method. Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings. editor / Alberto Del Bimbo ; Rita Cucchiara ; Stan Sclaroff ; Giovanni Maria Farinella ; Tao Mei ; Marco Bertini ; Hugo Jair Escalante ; Roberto Vezzani. Springer Science and Business Media Deutschland GmbH, 2021. pp. 291-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Visual simultaneous localization and mapping (SLAM) is a common solution for camera ego-positioning. However, SLAM sometimes loses tracking, for instance due to fast camera motion or featureless or repetitive environments. To account for the limitations of visual SLAM, we use sensor fusion method to fuse the visual positioning results with inertial measurement unit (IMU) data based on filter-based, loosely-coupled sensor fusion methods, and further combines feature-based SLAM with direct SLAM via proposed complementary fusion to retain the advantages of both methods; i.e., we not only keep the accurate positioning of feature-based SLAM but also account for its difficulty with featureless scenes by direct SLAM. Experimental results show that the proposed complementary method improves the positioning accuracy of conventional vision-only SLAM and leads to more robust positioning results.",

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author = "Kao, {Peng Yuan} and Tseng, {Kuan Wei} and Shen, {Tian Yi} and Song, {Yan Bin} and Chen, {Kuan Wen} and Hu, {Shih Wei} and Shih, {Sheng Wen} and Hung, {Yi Ping}",

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AU - Kao, Peng Yuan

AU - Tseng, Kuan Wei

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AU - Chen, Kuan Wen

AU - Hu, Shih Wei

AU - Shih, Sheng Wen

AU - Hung, Yi Ping

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N2 - Visual simultaneous localization and mapping (SLAM) is a common solution for camera ego-positioning. However, SLAM sometimes loses tracking, for instance due to fast camera motion or featureless or repetitive environments. To account for the limitations of visual SLAM, we use sensor fusion method to fuse the visual positioning results with inertial measurement unit (IMU) data based on filter-based, loosely-coupled sensor fusion methods, and further combines feature-based SLAM with direct SLAM via proposed complementary fusion to retain the advantages of both methods; i.e., we not only keep the accurate positioning of feature-based SLAM but also account for its difficulty with featureless scenes by direct SLAM. Experimental results show that the proposed complementary method improves the positioning accuracy of conventional vision-only SLAM and leads to more robust positioning results.

AB - Visual simultaneous localization and mapping (SLAM) is a common solution for camera ego-positioning. However, SLAM sometimes loses tracking, for instance due to fast camera motion or featureless or repetitive environments. To account for the limitations of visual SLAM, we use sensor fusion method to fuse the visual positioning results with inertial measurement unit (IMU) data based on filter-based, loosely-coupled sensor fusion methods, and further combines feature-based SLAM with direct SLAM via proposed complementary fusion to retain the advantages of both methods; i.e., we not only keep the accurate positioning of feature-based SLAM but also account for its difficulty with featureless scenes by direct SLAM. Experimental results show that the proposed complementary method improves the positioning accuracy of conventional vision-only SLAM and leads to more robust positioning results.

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Camera Ego-Positioning Using Sensor Fusion and Complementary Method

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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