The paper presents a novel approach for collaborative global localization that addresses the challenges of computational and communication constraints. The key highlights are:
The authors propose a method that reduces the amount of information exchanged and the computational cost for collaborative localization, while improving the overall localization performance.
They analyze and implement seminal approaches to collaborative localization, providing a unified overview and thorough analysis of alternative methods for compressing and exchanging belief distributions.
The authors release an open-source C++/ROS2 implementation of their approach as well as the baseline methods, which is a valuable contribution to the research community.
The proposed approach, called Compress++, exploits techniques for distribution compression in near-linear time with error guarantees. It outperforms the baseline methods in terms of success rate, convergence time, bandwidth requirements, and computational cost.
The authors evaluate their approach and the implemented baselines on multiple challenging scenarios, both in simulation and in the real world, demonstrating the practical applicability of their method.
The experiments show that the Compress++ approach can run online on an onboard computer, making it suitable for real-world deployment in resource-constrained multi-robot systems.
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by Nick... kl. arxiv.org 04-03-2024
https://arxiv.org/pdf/2404.02010.pdfDybere Forespørgsler