In this paper we address the problem of estimating the poses of a team of agents when they do not share any common reference frame. Each agent is capable of measuring the relative position and orientation of its neighboring agents, however these measurements are not exact but they are corrupted with noises. The goal is to compute the pose of each agent relative to an anchor node. We present a strategy where, first of all, the agents compute their orientations relative to the anchor. After that, they update the relative position measurements according to these orientations, to finally compute their positions. As contribution we discuss the proposed strategy, that has the interesting property that can be executed in a distributed fashion. The distributed implementation allows each agent to recover its pose using exclusively local information and local interactions with its neighbors. This algorithm has a low memory load, since it only requires each node to maintain an estimate of its own orientation and position.

Multi Agent Localization from Noisy Relative Pose Measurements / Aragues, R.; Carlone, Luca; Calafiore, Giuseppe Carlo; Sagues, C.. - STAMPA. - (2011), pp. 364-369. ((Intervento presentato al convegno IEEE Int. Conf. on Robotics and Automation tenutosi a Shanghai (China) nel 9–13 May 2011 [10.1109/ICRA.2011.5979799].

Multi Agent Localization from Noisy Relative Pose Measurements

CARLONE, LUCA;CALAFIORE, Giuseppe Carlo;
2011

Abstract

In this paper we address the problem of estimating the poses of a team of agents when they do not share any common reference frame. Each agent is capable of measuring the relative position and orientation of its neighboring agents, however these measurements are not exact but they are corrupted with noises. The goal is to compute the pose of each agent relative to an anchor node. We present a strategy where, first of all, the agents compute their orientations relative to the anchor. After that, they update the relative position measurements according to these orientations, to finally compute their positions. As contribution we discuss the proposed strategy, that has the interesting property that can be executed in a distributed fashion. The distributed implementation allows each agent to recover its pose using exclusively local information and local interactions with its neighbors. This algorithm has a low memory load, since it only requires each node to maintain an estimate of its own orientation and position.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2422942
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