Share Email Print
cover

Proceedings Paper

Localization using omnivision-based manifold particle filters
Author(s): Adelia Wong; Mohammed Yousefhussien; Raymond Ptucha
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Developing precise and low-cost spatial localization algorithms is an essential component for autonomous navigation systems. Data collection must be of sufficient detail to distinguish unique locations, yet coarse enough to enable real-time processing. Active proximity sensors such as sonar and rangefinders have been used for interior localization, but sonar sensors are generally coarse and rangefinders are generally expensive. Passive sensors such as video cameras are low cost and feature-rich, but suffer from high dimensions and excessive bandwidth. This paper presents a novel approach to indoor localization using a low cost video camera and spherical mirror. Omnidirectional captured images undergo normalization and unwarping to a canonical representation more suitable for processing. Training images along with indoor maps are fed into a semi-supervised linear extension of graph embedding manifold learning algorithm to learn a low dimensional surface which represents the interior of a building. The manifold surface descriptor is used as a semantic signature for particle filter localization. Test frames are conditioned, mapped to a low dimensional surface, and then localized via an adaptive particle filter algorithm. These particles are temporally filtered for the final localization estimate. The proposed method, termed omnivision-based manifold particle filters, reduces convergence lag and increases overall efficiency.

Paper Details

Date Published: 8 February 2015
PDF: 9 pages
Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 940606 (8 February 2015); doi: 10.1117/12.2080583
Show Author Affiliations
Adelia Wong, Rochester Institute of Technology (United States)
Mohammed Yousefhussien, Rochester Institute of Technology (United States)
Raymond Ptucha, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9406:
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top