Heading in the right direction

نویسندگان

چکیده

Stochastic topological models, and hidden Markov models in particular, are a useful tool for robotic navigation planning. In previous work we have shown how weak odometric data can be used to improve learning overcoming the common problems of standard Baum-Welch algorithm. Odometric typically contain directional information, which imposes two difficulties: First, cyclicity requires use special circular distributions. Second, small errors heading robot result large displacements readings it maintains. The cumulative rotational error leads unreliable readings. paper present solutions these by using distribution relative coordinate systems. We validate their effectiveness through experimental results from model-learning application.

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ژورنال

عنوان ژورنال: BDJ in practice

سال: 2021

ISSN: ['2057-3308', '2520-8675']

DOI: https://doi.org/10.1038/s41404-021-0884-3