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

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

Ananth Ranganathan, Michael Kaess and Frank Dellaert
Conference Paper, IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, pp. 2486-2493, October, 2007

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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.

BibTeX Reference
author = {Ananth Ranganathan and Michael Kaess and Frank Dellaert},
title = {Fast 3D Pose Estimation With Out-of-Sequence Measurements},
booktitle = {IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS},
year = {2007},
month = {October},
pages = {2486-2493},