Title: Decentralized Collaborative Localization with Deep GPS Coupling for UAVs
Author(s): Siddharth Tanwar, Grace Xingxin Gao
Published in: Proceedings of IEEE/ION PLANS 2018
April 23 - 26, 2018
Hyatt Regency Hotel
Monterey, CA
Pages: 767 - 774
Cite this article: Tanwar, Siddharth, Gao, Grace Xingxin, "Decentralized Collaborative Localization with Deep GPS Coupling for UAVs," Proceedings of IEEE/ION PLANS 2018, Monterey, CA, April 2018, pp. 767-774.
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Abstract: GPS navigation in urban environments is prone to error sources such as multipath and signal blockage. However, if we consider several agents, it is highly likely that some agents have better localization capabilities due to different views of the sky, heterogeneity in sensors etc. Collaborative localization (CL) is a way to aid navigation in a multi-agent system. CL algorithms face challenges such as scalability, robustness to noisy sensor data and single point of failure, and operability despite limited inter-agent communication. In this paper, we present a decentralized collaborative localization algorithm which is asynchronous, applicable to sparsely communicating networks, and has minimal information exchange. Moreover, the proposed algorithm takes advantage of the variable visibility of the sky for different agents. We propose a methodology for relaying satellite information between agents to augment the set of visible satellites on each agent with virtual satellites, thereby providing more constraint equations to each agent. The methodology is based on coupling of agents’ GPS measurements with range-only sensors and is applicable to multi-agent systems with these modalities. The proposed method is validated on real world dataset involving an aerial vehicle, ground agents, and several range-only sensors.