Joint tracking-registration with linear complexity: an application to range sensors

Authors:
Shuqing Zeng;Yanhua Chen
Affiliations:
Electrical & Controls Integration Laboratory, General Motors R&D Center, Warren, MI;Department of Computer Science, Wayne State University, Detroit, MI
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 3
Cited 0

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An exact maximum likelihood registration algorithm for data fusion

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Abstract

Sensor fusion of multiple sources plays an important role in robotic systems to achieve refined target position and velocity estimates. In this paper, we address the general registration problem, which is a key module for a fusion system to accurately correct systematic errors of sensors. A fast maximum a posteriori (FMAP) algorithm for joint registration and tracking is presented. The algorithm uses a recursive two-step optimization that involves orthogonal factorization to ensure numerically stability. Statistical efficiency analysis based on Cramèr-Rae lower bound theory is presented to show asymptotical optimality of FMAP. Also, Givens rotation is used to derive a fast implementation with complexity O(n) (n denoting number of targets). Experiments arc presented to demonstrate the promise and effectiveness of FMAP.