Recursive decentralized localization for multi-robot systems with asynchronous pairwise communication
| Autor: | Tobias Schubert, Wolfram Burgard, Lukas Luft, Stergios I. Roumeliotis |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Cross-correlation Computer science Applied Mathematics Mechanical Engineering 010401 analytical chemistry 02 engineering and technology Track (rail transport) 01 natural sciences 0104 chemical sciences Extended Kalman filter 020901 industrial engineering & automation Robotic systems Artificial Intelligence Asynchronous communication Modeling and Simulation Pairwise comparison Electrical and Electronic Engineering Algorithm Software |
| Zdroj: | The International Journal of Robotics Research. 37:1152-1167 |
| ISSN: | 1741-3176 0278-3649 |
| DOI: | 10.1177/0278364918760698 |
| Popis: | This paper provides a fully decentralized algorithm for collaborative localization based on the extended Kalman filter. The major challenge in decentralized collaborative localization is to track inter-robot dependencies, which is particularly difficult when sustained synchronous communication between the robots cannot be guaranteed. Current approaches suffer from the need for particular communication schemes, extensive bookkeeping of measurements, overly conservative assumptions, or the restriction to specific measurement models. This paper introduces a localization algorithm that is able to approximate the inter-robot correlations while fulfilling all of the following conditions: communication is limited to two robots that obtain a relative measurement, the algorithm is recursive in the sense that it does not require storage of measurements and each robot maintains only the latest estimate of its own pose, and it supports generic measurement models. The fact that the proposed approach can handle these particularly difficult conditions ensures that it is applicable to a wide range of multi-robot scenarios. We provide mathematical details on our approximation. Extensive experiments carried out using real-world datasets demonstrate the improved performance of our method compared with several existing approaches. |
| Databáze: | OpenAIRE |
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