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In this paper, we introduce a method for recognizing a subject complex object in real world environment. We use a three dimensional model described by line segments of the object and the data provided by a three-axis orientation sensor attached to the video camera. We assume that existing methods for finding line features in the image allow at least one model line segment to be detected as a single continuous segment. The method consists of two main steps: generation of pose hypotheses and then evaluation of each pose in order to select the most appropriate one. The first stage is three-fold: model visibility, line matching and pose estimation; the second stage aims to rank the poses by evaluating the similarity between the projected model lines and the image lines. Furthermore, we propose an additional step that consists of refining the best candidate pose by using the Lie group formalism of spatial rigid motions. Such a formalism provides an efficient local parameterization of the set of rigid rotation via the exponential map. A set of experiments demonstrating the robustness of this approach is presented.

Accurate Object Recognition Using Orientation Sensor with Refinement on the Lie Group of Spatial Rigid Motions