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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Inferring and Enforcing Relative Constraints in SLAM
verfasst von : Kristopher R. Beevers, Wesley H. Huang
Erschienen in: Algorithmic Foundation of Robotics VII
Verlag: Springer Berlin Heidelberg
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Most algorithms for simultaneous localization and mapping (
slam
) do not incorporate prior knowledge of structural or geometrical characteristics of the environment. In some cases, such information is readily available and making some assumptions is reasonable. For example, one can often assume that many walls in an indoor environment are rectilinear. In this paper, we develop a
slam
algorithm that incorporates prior knowledge of relative constraints between landmarks. We describe a “Rao-Blackwellized constraint filter” that infers applicable constraints and efficiently enforces them in a particle filtering framework. We have implemented our approach with rectilinearity constraints. Results from simulated and real-world experiments show the use of constraints leads to consistency improvements and a reduction in the number of particles needed to build maps.