Abstract-Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.

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An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual robots. This paper presents a novel approach to multi-robot map merging under global uncertainty about the robot's relative locations. Our approach uses an adapted version of particle filters to estimate the position of one robot in the other robot's partial map. The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach. We show how to seamlessly integrate this approach into a decision-theoretic multi-robot coordination strategy. The experiments show that our sample-based technique can reliably find good hypotheses for map matches. Furthermore, we present results obtained with two robots successfully merging their maps using the decision-theoretic rendezvous strategy.

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