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Régis Vincent

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IROS Conference 2010 Conference Paper

Efficient Sparse Pose Adjustment for 2D mapping

  • Kurt Konolige
  • Giorgio Grisetti
  • Rainer Kümmerle
  • Wolfram Burgard
  • Benson Limketkai
  • Régis Vincent

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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AAAI Conference 1999 Conference Paper

Learning Quantitative Knowledge for Multiagent Coordination

  • David Jensen
  • Michael Atighetchi
  • Régis Vincent
  • Victor Lesser
  • University of Massachusetts at Amherst

A central challenge of multiagent coordination is reasoning about howthe actions of one agent affect the actions of another. Knowledge of these interrelationships can help coordinate agents -- preventing conflicts and exploiting beneficial relationships among actions. Weexplore three interlocking methods that learn quantitative knowledge of such non-local effects in T/EMS, a well-developed frameworkfor multiagent coordination. Thesurprising simplicity and effectiveness of these methods demonstrates howagents can learn domain-specificknowledge quickly, extendingthe utility of coordination frameworks that explicitly represent coordination knowledge.

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