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Efficient Distributed Particle Filter for Robust Range-Only SLAM
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AI
DOI：10.1109/JIOT.2022.3181994.png)
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摘要
En 中文
Compared with simultaneous localization and mapping (SLAM) problems based on Lidar or visual sensors, range-only SLAM (RO-SLAM) is lacking bearing information. It brings challenges to particle sampling and SLAM estimation. This article proposes an efficient distributed particle filter (EDPF) for RO-SLAM problems. To overcome the difficulties of sampling in a high-dimensional state space, EDPF is decomposed into a set of subparticle filters (sub-PFs) with low dimensionality. Each sub-PF corresponds with an observed beacon, which directly reduces the sampling complexity. A joint weight update method is proposed to exploit the correlation among sub-PFs. It reweighs the particles via an auxiliary distribution after each sub-PF's local filtering and embodies a better proposal distribution for distributed filtering. In addition, a beacon diagnosis method is proposed, it can detect and reinitialize the wrong converged beacon position estimates, which further reduces the SLAM error accumulation problem. We consider a RO-SLAM system with an odometer and ultrawideband (UWB) to verify the proposed EDPF. Results show that EDPF outperforms many existing RO-SLAM methods, which obtains the best performance with the acceptable computational load.
Keyword:
Robots
Distance measurement
Simultaneous localization and mapping
Extraterrestrial measurements
Filtering
Particle measurements
Particle filters
Auxiliary distribution
joint weight update
particle filter (PF)
range-only simultaneous localization and mapping (RO-SLAM)
ultrawideband (UWB)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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