Fast 3D Pose Estimation With Out-of-Sequence Measurements

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“Fast 3D Pose Estimation With Out-of-Sequence Measurements” by A. Ranganathan, M. Kaess, and F. Dellaert. In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, (San Diego, CA), Oct. 2007, pp. 2486-2493.


We present an algorithm for pose estimation using fixed-lag smoothing. We show that fixed-lag smoothing enables inclusion of measurements from multiple asynchronous measurement sources in an optimal manner. Since robots usually have a plurality of uncoordinated sensors, our algorithm has an advantage over filtering-based estimation algorithms, which cannot incorporate delayed measurements optimally. We provide an implementation of the general fixed-lag smoothing algorithm using square root smoothing, a technique that has recently become prominent. Square root smoothing uses fast sparse matrix factorization and enables our fixed-lag pose estimation algorithm to run at upwards of 20 Hz. Our algorithm has been extensively tested over hundreds of hours of operation on a robot operating in outdoor environments. We present results based on these tests that verify our claims using wheel encoders, visual odometry, and GPS as sensors.

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BibTeX entry:

   author = {A. Ranganathan and M. Kaess and F. Dellaert},
   title = {Fast 3{D} Pose Estimation With Out-of-Sequence Measurements},
   booktitle = {Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and
	Systems, IROS},
   pages = {2486-2493},
   address = {San Diego, CA},
   month = oct,
   year = {2007}
Last updated: March 21, 2023