Lazy localization using the Frozen-Time Smoother

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Lazy localization using the Frozen-Time Smoother
— We present a new algorithm for solving the global localization problem called Frozen-Time Smoother (FTS). Time is ‘frozen’, in the sense that the belief always refers to the same time instant, instead of following a moving target, like Monte Carlo Localization does. This algorithm works in the case in which global localization is formulated as a smoothing problem, and a precise estimate of the incremental motion of the robot is usually available. These assumptions correspond to the case when global localization is used to solve the loop closing problem in SLAM. We compare FTS to two Monte Carlo methods designed with the same assumptions. The experiments suggest that a naive implementation of the FTS is more efficient than an extremely optimized equivalent Monte Carlo solution. Moreover, the FTS has an intrinsic laziness: it does not need frequent updates (scans can be integrated once every many meters) and it can process data in arbitrary order. The source code and datasets ar...
Andrea Censi, Gian Diego Tipaldi
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICRA
Authors Andrea Censi, Gian Diego Tipaldi
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