Consistent data association in multi-robot systems with limited communications

Abstract

Abstract-In this paper we address the data association problem of features observed by a robot team with limited communications. At every time instant, each robot can only exchange data with a subset of the robots, its neighbors. Initially, each robot solves a local data association with each of its neighbors. After that, the robots execute the proposed algorithm to agree on a data association between all their local observations which is globally consistent. One inconsistency appears when chains of local associations give rise to two features from one robot being associated among them. The contribution of this work is the decentralized detection and resolution of these inconsistencies. We provide a fully decentralized solution to the problem. This solution does not rely on any particular communication topology. Every robot plays the same role, making the system robust to individual failures. Information is exchanged exclusively between neighbors. In a finite number of iterations, the algorithm finishes with a data association which is free of inconsistent associations.In the experiments, we show the performance of the algorithm under two scenarios. In the first one, we apply the resolution and detection algorithm for a set of stochastic visual maps. In the second, we solve the feature matching between a set of images taken by a robotic team.

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

“…A data cache filters out already received information. Similarly, in [228] and [229], a consensus is sought between neighbors so as to find the best map possible that avoids double-counting information. In [113], each vehicle state (pose and map) is stored separately from the others.…”

Feature-based map merging with dynamic consensus on information increments

“…Alternatively, information can be processed in the form of local maps, and these local maps can be kept independent by avoiding the introduction of global information into them; this is what we propose here, and it is also the approach followed in [21]. This strategy has the benefit that each robot can produce meaningful representations of the environment, which allows for several high level data association methods [21], [22]. Not introducing global data in the local maps, has the effect of keeping the local maps of different robots independent.…”

“…(3). In practice, robots can execute distributed data association methods [21], [22], [31] for feature-based maps to obtain these relationships. Robots discover new features in the information received from their neighbors, and introduce additional rows and columns in the information matrices and vectors for them.…”

Distributed consensus algorithms for merging feature-based maps with limited communication

“…After n iterations the resolution algorithm finishes, with the size of each conflictive set being reduced by at least 2m features [29,Theorem 4.3]. When the algorithm finishes, each feature f i r that has been assigned to a component (i , r ) has become consistent due to the edge removals (34).…”

“…Section 4 addresses the problem of computing the data association between the maps acquired by the robots. A preliminary version of this work appeared in [29]. Here, an improved algorithm is presented that simultaneously builds a set of labels for the features which reflects the global data association.…”