Model supported positioning

Richard W. Ely; Joseph A. Di Girolamo; James C. Lundgren

doi:10.1117/12.213133

5 July 1995 Model-supported positioning

Richard W. Ely, Joseph A. Di Girolamo, James C. Lundgren

Proceedings Volume 2486, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision II; (1995) https://doi.org/10.1117/12.213133
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

We discuss Texas Instrument's approach to the Model Supported Positioning (MSP) Project. There are two main goals of the MSP project. The first is to meet the model-to-image registration needs of the Research and Development for Image Understanding Systems (RADIUS) Project. The second goal is to provide an automated capability for deriving more accurate absolute image position information using a scene model with accurate ground control positions. The approach is to use model-to-image registration to locate features in the image whose absolute 3 - 4 positions are precisely known from the model. Rigorous photogrammetry is used to refine the image acquisition parameters by minimizing the error between the known 3D positions and the image derived positions. While we briefly discuss the parameter refinement step, the emphasis of this paper is on the registration algorithm. We first estimate a coarse offset error vector between the image and the model. This estimate is from a vector registration algorithm which matches straight lines extracted from a reduced resolution image to lines from the site model. The image location of each 3D control feature is then determined using oriented line and vertex operators in the full-resolution image. A least-squares approach is used to refine the image acquisition parameters to match the known control feature positions.

Citation Download Citation

Richard W. Ely, Joseph A. Di Girolamo, and James C. Lundgren "Model-supported positioning", Proc. SPIE 2486, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision II, (5 July 1995); https://doi.org/10.1117/12.213133

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