Multi-Sensor Data Fusion for Estimation of a Moving Polyhedral Object

Ren C. Luo; Woo Suk Yang

doi:10.1117/12.969967

1 March 1990 Multi-Sensor Data Fusion for Estimation of a Moving Polyhedral Object

Ren C. Luo, Woo Suk Yang

Author Affiliations +

Proceedings Volume 1198, Sensor Fusion II: Human and Machine Strategies; (1990) https://doi.org/10.1117/12.969967
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

This paper presents an approach to estimate the general 3D motion of a polyhedral object using multiple sensor data some of which may not provide, sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneuous state of an object. The instantaneuous state of an object is specified by the rotation, which is defined by a rotation axis and rotation angle, and the displacement of the center of rotation. We have introduced a method based on Moore-Penrose pseudoinverse theory to estimate the instantaneuous state of an object, and a linear feedback estimation algorithm to approach the motion estimation. The motion estimated from each sensor is fused-to provide more accurate and reliable information about the motion of an unknown object. The techniques of multi-sensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision. With the assumption that the motion is smooth, our approach can handle the data sequences from multiple sensors with different sampling times. We can also predict the next immediate object position and its motion.

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Ren C. Luo and Woo Suk Yang "Multi-Sensor Data Fusion for Estimation of a Moving Polyhedral Object", Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); https://doi.org/10.1117/12.969967

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KEYWORDS

Sensors

Motion estimation

Error analysis

Sensor fusion

Data fusion

Matrices

Data modeling

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