Share Email Print

Proceedings Paper

A simple map-based localization strategy using range measurements
Author(s): Kevin L. Moore; Aliasgar Kutiyanawala; Madhumita Chandrasekharan
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

In this paper we present a map-based approach to localization. We consider indoor navigation in known environments based on the idea of a "vector cloud" by observing that any point in a building has an associated vector defining its distance to the key structural components (e.g., walls, ceilings, etc.) of the building in any direction. Given a building blueprint we can derive the "ideal" vector cloud at any point in space. Then, given measurements from sensors on the robot we can compare the measured vector cloud to the possible vector clouds cataloged from the blueprint, thus determining location. We present algorithms for implementing this approach to localization, using the Hamming norm, the 1-norm, and the 2-norm. The effectiveness of the approach is verified by experiments on a 2-D testbed using a mobile robot with a 360° laser range-finder and through simulation analysis of robustness.

Paper Details

Date Published: 27 May 2005
PDF: 9 pages
Proc. SPIE 5804, Unmanned Ground Vehicle Technology VII, (27 May 2005); doi: 10.1117/12.604416
Show Author Affiliations
Kevin L. Moore, Johns Hopkins Univ. Applied Physics Lab. (United States)
Aliasgar Kutiyanawala, Utah State Univ. (United States)
Madhumita Chandrasekharan, Utah State Univ. (United States)

Published in SPIE Proceedings Vol. 5804:
Unmanned Ground Vehicle Technology VII
Grant R. Gerhart; Charles M. Shoemaker; Douglas W. Gage, Editor(s)

© SPIE. Terms of Use
Back to Top