Distributed Consistent Multi-Robot Semantic Localization and Mapping

TL;DR

This paper introduces a novel multi-robot semantic SLAM method that maintains a distributed hybrid belief over localization and classification, improving accuracy in unknown environments with viewpoint-dependent object appearances.

Contribution

It presents the first approach to achieve consistent estimation of both continuous localization and discrete classification variables in multi-robot semantic mapping.

Findings

Enhanced classification accuracy in multi-robot scenarios

Improved localization precision over local methods

Validated in simulation and real-world experiments

Abstract

We present an approach for multi-robot consistent distributed localization and semantic mapping in an unknown environment, considering scenarios with classification ambiguity, where objects' visual appearance generally varies with viewpoint. Our approach addresses such a setting by maintaining a distributed posterior hybrid belief over continuous localization and discrete classification variables. In particular, we utilize a viewpoint-dependent classifier model to leverage the coupling between semantics and geometry. Moreover, our approach yields a consistent estimation of both continuous and discrete variables, with the latter being addressed for the first time, to the best of our knowledge. We evaluate the performance of our approach in a multi-robot semantic SLAM simulation and in a real-world experiment, demonstrating an increase in both classification and localization accuracy compared to maintaining a hybrid belief using local information only.