Feature correspondence in multiple sensor data fusion

Ted J. Broida

doi:10.1117/12.25303

1 April 1991 Feature correspondence in multiple sensor data fusion

Ted J. Broida

Proceedings Volume 1383, Sensor Fusion III: 3D Perception and Recognition; (1991) https://doi.org/10.1117/12.25303
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

Before combining measurement data from multiple sensors, it is first necessary to identify those measurements that correspond to the same “world” feature or target. This paper addresses three topics related to the problem of establishing feature correspondence (data association). First, a standard maximum likelihood (ML) decision rule for feature correspondence is reviewed, emphasizing the relationship between the decision rule and the Kalman filter model structure. Next, four measurement “primitives” are developed as a convenient data represention for fusion of diverse measurements. First-order models can be constructed from these primitives for a wide range of sensor types and applications. Finally, these ideas are illustrated with examples involving stereo (binocular) image feature correspondence and fusion, starting first with a two dimensional example which is then generalized to the three dimensional case of practical interest. A novel method is presented for registering observed features from two (or more) cameras that provides “triangulation” range estimates along with feature correspondence statistics. The stereo image association problem is also addressed for the case when both cameras measure optical flow (angle-rate) of discrete features.

Citation Download Citation

Ted J. Broida "Feature correspondence in multiple sensor data fusion", Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); https://doi.org/10.1117/12.25303

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

Includes PDF, HTML & Video, when available