DSFS: Dynamic Sensor Fusion System for Robust Localization with Diverse Sensing Information

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I whakaputaina i:Journal of Robotics and Mechatronics vol. 37, no. 5 (Oct 2025), p. 1127
Kaituhi matua: Suzuki, Takumi
Ētahi atu kaituhi: Funabora Yuki, Doki Shinji, Doki Kae, Yamazumi Mitsuhiro
I whakaputaina:
Fuji Technology Press Co. Ltd.
Ngā marau:
Urunga tuihono:Citation/Abstract
Full Text - PDF
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024 7 |a 10.20965/jrm.2025.p1127  |2 doi 
035 |a 3262830713 
045 2 |b d20251001  |b d20251031 
100 1 |a Suzuki, Takumi  |u Nagoya University Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan suzuki.takumi.v4@s.mail.nagoya-u.ac.jp 
245 1 |a DSFS: Dynamic Sensor Fusion System for Robust Localization with Diverse Sensing Information 
260 |b Fuji Technology Press Co. Ltd.  |c Oct 2025 
513 |a Journal Article 
520 3 |a Autonomous mobile robots are expected to demonstrate a high degree of adaptability, enabling effective operation across diverse environments. As sensor performance is largely influenced by environmental conditions, relying solely on a single sensor for localization makes robust localization challenging. To address this issue, various studies have enhanced the localization robustness using multiple sensors with different characteristics that complement each other’s weaknesses. However, conventional studies require the design of separate fusion systems for each type and numerous sensor observations. As future developments facilitate increased cooperation with environment-fixed sensors and external agents, the types and number of sensor observations accessible to robots are expected to dynamically change depending on location and time. Therefore, a pose fusion system that adapts to such changes is required. This paper proposes a fusion system that can adapt to changes in the type and number of sensor observations. This system dynamically fuses pose information obtained from onboard sensors, environment-fixed sensors, and external agents by extending the selective fusion method, one of the existing pose fusion methods for onboard sensors. Simulation experiments confirm that our system can adapt to changes in the type and number of sensor observations and robustly localize by dynamically fusing pose information from onboard sensors, environment-fixed sensors, and external agents. 
653 |a Multisensor applications 
653 |a Robots 
653 |a Sensors 
653 |a Localization 
653 |a Robustness 
653 |a Multisensor fusion 
700 1 |a Funabora Yuki  |u Nagoya University Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan suzuki.takumi.v4@s.mail.nagoya-u.ac.jp 
700 1 |a Doki Shinji  |u Nagoya University Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan suzuki.takumi.v4@s.mail.nagoya-u.ac.jp 
700 1 |a Doki Kae  |u Aichi Institute of Technology 1247 Yachigusa, Yakusa-cho, Toyota, Aichi 470-0392, Japan 
700 1 |a Yamazumi Mitsuhiro  |u Mitsubishi Electric Corporation 8-1-1 Tsukaguchi-Honmachi, Amagasaki, Hyogo 661-8661, Japan 
773 0 |t Journal of Robotics and Mechatronics  |g vol. 37, no. 5 (Oct 2025), p. 1127 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3262830713/abstract/embedded/160PP4OP4BJVV2EV?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3262830713/fulltextPDF/embedded/160PP4OP4BJVV2EV?source=fedsrch