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- Title
Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter.
- Authors
Hao, Chen; Chengju, Liu; Qijun, Chen
- Abstract
SUMMARY: Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.
- Subjects
HUMANOID robots; KALMAN filtering; UNITS of measurement; MULTISENSOR data fusion; ODOMETERS
- Publication
Robotica, 2021, Vol 39, Issue 2, p1216
- ISSN
0263-5747
- Publication type
Article
- DOI
10.1017/S0263574720001046