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• W2896711393 abstract "Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi-robot teams. We designed a stereo vision-based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real-time, long-term stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra- as well as inter-robot loop closures through an improved submap matching method to provide global multi-robot pose and map estimates; (3) Distribution of the processing of high-frequency and high-bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real-world datasets and present our full system in five real-world multi-robot experiments in areas of up 3,000 m2 (bounding box), including visual robot detections and submap matches as loop-closure constraints. Further, we demonstrate its application to autonomous multi-robot exploration in a challenging rough-terrain environment at a Moon-analogue site located on a volcano." @default.
• W2896711393 created "2018-10-26" @default.
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• W2896711393 date "2018-10-17" @default.
• W2896711393 modified "2023-10-14" @default.
• W2896711393 title "Distributed stereo vision-based 6D localization and mapping for multi-robot teams" @default.
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• W2896711393 doi "https://doi.org/10.1002/rob.21812" @default.
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