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Many fielded mobile robot systems have demonstrated the importance of directly estimating the 3D shape of objects in the robot's vicinity. The most mature solutions available today use active laser scanning or stereo camera pairs, but both approaches require specialized and expensive sensors. In prior publications, we have demonstrated the generation of stereo images from a single very low-cost camera using structure from motion (SFM) techniques. In this paper we demonstrate the practical usage of single-camera stereo in real-world mobile robot applications. Stereo imagery tends to produce incomplete 3D shape reconstructions of man-made objects because of smooth/glary regions that defeat stereo matching algorithms. We demonstrate robust object detection despite such incompleteness through matching of simple parameterized geometric models. Results are presented where parked cars are detected, and then recognized via license plate recognition, all in real time by a robot traveling through a parking lot.

Over the last 30 years, scale space representations have emerged as a fundamental tool for allowing systems to become increasingly robust against changes in camera viewpoint. Unfortunately, the implementation details that are required to properly construct a scale space representation are not published in the literature. Incorrectly implementing these details will lead to extremely poor system performance. In this paper, we address the practical considerations associated with scale space representations. Our focus is to make explicit how a scale space is constructed, thereby increasing the accessibility of this powerful representation to developers of computer vision systems.