NDLI: Continuous-time batch estimation using temporal basis functions

Content Provider	IEEE Xplore Digital Library
Author	Furgale, P. Barfoot, T.D. Sibley, G.
Copyright Year	2012
Description	Author affiliation: University of Toronto, Canada (Barfoot, T.D.) \|\| George Washington University, USA (Sibley, G.) \|\| ETH Zürich, Switzerland (Furgale, P.)
Abstract	Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable. However, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping (SLAM) problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full maximum likelihood estimation (MLE) problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian, and show how coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms through the application of different assumptions at any point. We use the SLAM problem as our motivation throughout the paper, although the approach is not specific to this application. Results from a self-calibration experiment involving a camera and a high-rate inertial measurement unit are provided to validate the approach.
Starting Page	2088
Ending Page	2095
File Size	1761944
Page Count	8
File Format	PDF
ISBN	9781467314039
ISSN	10504729
e-ISBN	9781467314053
e-ISBN	9781467314046
DOI	10.1109/ICRA.2012.6225005
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping Estimation Time measurement Splines (mathematics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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