NDLI: A convex optimization based approach for pose SLAM problems

Content Provider	IEEE Xplore Digital Library
Author	Minjie Liu Shoudong Huang Dissanayake, G. Heng Wang
Copyright Year	2012
Description	Author affiliation: College of Electronic Information and Control Engineering, Beijing University of Technology, 100124, China (Heng Wang) \|\| Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia (Minjie Liu; Shoudong Huang; Dissanayake, G.)
Abstract	This paper demonstrates that 2D pose SLAM has an underlining near convex structure when formulated as a least squares (LS) optimization problem. By introducing new variables and some approximations, the LS pose SLAM problem can be formulated as a quadratically constrained quadratic programming (QCQP) problem. The QCQP formulation can then be relaxed into a semi-definite programming (SDP) problem which is convex. Unique solution to the convex SDP problem can be obtained without initial state estimate and can be used to construct a candidate solution to the original LS pose SLAM problem. Simulation datasets and the Intel Research Lab dataset have been used to demonstrate that when the relative pose information contain noises with reasonable level, the candidate solution obtained through the relaxation is very close to the optimal solution to the LS SLAM problem. Thus in practice, the candidate solution can serve as either an approximate solution or a good initial guess for a local optimization algorithm to obtain the optimal solution to the LS pose SLAM problem.
Starting Page	1898
Ending Page	1903
File Size	420505
Page Count	6
File Format	PDF
ISBN	9781467317375
ISSN	21530858
e-ISBN	9781467317368
DOI	10.1109/IROS.2012.6385742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-07
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping Linear programming Covariance matrix Vectors Approximation methods Optimization
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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