Merging of appearance-based place knowledge among multiple robots

QC 20200707
If robots can merge the appearance-based place knowledge of other robots with their own, they can relate to these places even if they have not previously visited them. We have investigated this problem using robots with compatible visual sensing capabilities and with each robot having its individual long-term place memory. Here, each place refers to a spatial region as defined by a collection of appearances and in the place memory, the knowledge is organized in a tree hierarchy. In the proposed merging approach, the hierarchical organization plays a key role—as it corresponds to a nested sequence of hyperspheres in the appearance space. The merging proceeds by considering the extent of overlap of the respective nested hyperspheres—starting with the largest covering hypersphere. Thus, differing from related work, knowledge is merged in as large chunks as possible while the hierarchical structure is preserved accordingly. As such, the merging scales better as the extent of knowledge to be merged increases. This is demonstrated in an extensive set of multirobot experiments where robots share their knowledge and then use their merged knowledge when visiting these places.

NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
Multi-robot
Place recognition
Unsupervised learning
Agricultural robots
Industrial robots
Mergers and acquisitions
Appearance based
Hierarchical organizations
Hierarchical structures
Multiple robot
Multirobots
Nested sequences
Spatial regions
Merging

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NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Datorseende och ...

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