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Long range stereo data-fusion from m...

Sibley, Gabe.

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Long range stereo data-fusion from moving platforms.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Long range stereo data-fusion from moving platforms./
Author:	Sibley, Gabe.
Description:	154 p.
Notes:	Advisers: Gaurav Sukhatme; Larry Matthies.
Contained By:	Dissertation Abstracts International68-10B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283541
ISBN:	9780549251156

Long range stereo data-fusion from moving platforms.
Sibley, Gabe.

Long range stereo data-fusion from moving platforms. - 154 p.

Advisers: Gaurav Sukhatme; Larry Matthies.

Thesis (Ph.D.)--University of Southern California, 2007.

This work is concerned with accurate and precise long-range depth estimation by filtering sequences of images. We investigate the problem with both stationary and mobile cameras. We find that with normally distributed sensor noise the non-linear properties of stereo triangulation, either from a motion baseline or from a stereo rig, can lead to statistically biased range estimates. In the static camera case we develop two novel filters to overcome this: a second order Gauss-Newton filter, and an Iterated Sigma Point Kalman filter. For the moving sensor case we develop a sliding window filter for Simultaneous Localization and Mapping that concentrates computational resources on accurately estimating the immediate spatial surroundings by using a sliding time window of the most recent sensor measurements. The sliding window method exhibits many interesting properties, like reversible data association, constant time complexity during exploration, and robust estimation across multiple timesteps. Importantly, the sliding window filter has the potential to approximate the batch estimator in terms of optimality and efficiency. To this end we demonstrate convergence results using real and simulated image data.

ISBN: 9780549251156Subjects--Topical Terms:

626642
Computer Science.

Long range stereo data-fusion from moving platforms.
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020 $a 9780549251156
035 $a (UMI)AAI3283541
035 $a AAI3283541
040 $a UMI $c UMI
100 1 $a Sibley, Gabe. $3 1278494
245 1 0 $a Long range stereo data-fusion from moving platforms.
300 $a 154 p.
500 $a Advisers: Gaurav Sukhatme; Larry Matthies.
500 $a Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6775.
502 $a Thesis (Ph.D.)--University of Southern California, 2007.
520 $a This work is concerned with accurate and precise long-range depth estimation by filtering sequences of images. We investigate the problem with both stationary and mobile cameras. We find that with normally distributed sensor noise the non-linear properties of stereo triangulation, either from a motion baseline or from a stereo rig, can lead to statistically biased range estimates. In the static camera case we develop two novel filters to overcome this: a second order Gauss-Newton filter, and an Iterated Sigma Point Kalman filter. For the moving sensor case we develop a sliding window filter for Simultaneous Localization and Mapping that concentrates computational resources on accurately estimating the immediate spatial surroundings by using a sliding time window of the most recent sensor measurements. The sliding window method exhibits many interesting properties, like reversible data association, constant time complexity during exploration, and robust estimation across multiple timesteps. Importantly, the sliding window filter has the potential to approximate the batch estimator in terms of optimality and efficiency. To this end we demonstrate convergence results using real and simulated image data.
590 $a School code: 0208.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Robotics. $3 1018454
690 $a 0771
690 $a 0984
710 2 $a University of Southern California. $b Computer Science: Doctor of Philosophy. $3 1026068
773 0 $t Dissertation Abstracts International $g 68-10B.
790 $a 0208
790 1 0 $a Matthies, Larry, $e advisor
790 1 0 $a Mendel, Jerey $e committee member
790 1 0 $a Schaal, Stefan $e committee member
790 1 0 $a Sukhatme, Gaurav, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283541