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Lazy localization using the Frozen-Time Smoother

Censi, A. and Tipaldi, G. D. (2008) Lazy localization using the Frozen-Time Smoother. In: IEEE International Conference on Robotics and Automation, 2008. IEEE , pp. 2778-2783. ISBN 9781424416462.

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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 are available for download.

Item Type:Book Section
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Censi, A.0000-0001-5162-0398
Additional Information:© 2008 IEEE. Issue Date: 19-23 May 2008; Date of Current Version: 13 June 2008.
Subject Keywords:SLAM (robots); mobile robots ; SLAM , frozen-time smoother , incremental robot motion , lazy localization problem , simultaneous localization and mapping
Record Number:CaltechAUTHORS:20100512-152546508
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Official Citation:Censi, A.; Tipaldi, G.D.; , "Lazy localization using the Frozen-Time Smoother," Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on , vol., no., pp.2778-2783, 19-23 May 2008 doi: 10.1109/ROBOT.2008.4543631 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18272
Deposited On:03 Jun 2010 21:21
Last Modified:08 Nov 2021 23:42

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