Robust position estimation of a mobile vehicle

Vania Conan; Pierre Boulanger; Shadia Elgazzar

doi:10.1117/12.193945

9 November 1994 Robust position estimation of a mobile vehicle

Vania Conan, Pierre Boulanger, Shadia Elgazzar

Author Affiliations +

Proceedings Volume 2247, Sensors and Control for Automation; (1994) https://doi.org/10.1117/12.193945
Event: Optics for Productivity in Manufacturing, 1994, Frankfurt, Germany

Abstract

The ability to estimate the position of a mobile vehicle is a key task for navigation over large distances in complex indoor environments such as nuclear power plants. Schematics of the plants are available, but they are incomplete, as real settings contain many objects, such as pipes, cables or furniture, that mask part of the model. The position estimation method described in this paper matches 3-D data with a simple schematic of a plant. It is basically independent of odometry information and viewpoint, robust to noisy data and spurious points and largely insensitive to occlusions. The method is based on a hypothesis/verification paradigm and its complexity is polynomial; it runs in (Omicron) (m⁴n⁴), where m represents the number of model patches and n the number of scene patches. Heuristics are presented to speed up the algorithm. Results on real 3-D data show good behavior even when the scene is very occluded.

Citation Download Citation

Vania Conan, Pierre Boulanger, and Shadia Elgazzar "Robust position estimation of a mobile vehicle", Proc. SPIE 2247, Sensors and Control for Automation, (9 November 1994); https://doi.org/10.1117/12.193945

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE