Owing to their large field of view, a catadioptric imaging system is widely used in several research areas, including robot navigation, surveillance, and three‐dimensional (3D) reconstruction. The modelling and calibration of these systems allow the accurate measurement of objects. This paper presents a calibration method for a panoramic 3D reconstruction system based on pattern projection composed of two modules. The first module, which contains a charged coupled device camera and a parabolic mirror allows the acquisition of catadioptric images. The second module is a catadioptric projection system made of a light projector and a parabolic mirror that generates a projected pattern over the target for reconstruction purposes. Both the modules are calibrated independently. The method achieves a reprojection error of 0.6 pixels in the camera module and 1.2 pixels in the catadioptric projection system. To evaluate the performance of the proposed method, the results show 3D reconstructions of multiple objects with tolerances around 1.3 mm.

In this document, we study the problem of optimally placing a mixture of directional and omnidirectional cameras. In our solution, the workspace is represented by an occupancy grid map [1]. Then, using surface-projected workspace and camera perception models, we develop a binary integer programming algorithm. The results of the algorithm are applied successfully to a variety of simulated scenarios.

In this work a proposal to model the activity at vehicular intersections with the aim of detecting unusual events is presented. Using the particular constraints that this kind of scenarios provide, we develop methods to detect, track, and model the activity of moving objects. Our description of activity is based on the local definition, at each pixel, of a multimodal model for the direction of motion. During operation, a particular observation is compared with the learned model. Our experiments give clear indication that the proposed scheme has a good performance in detecting such unusual events as vehicles running on red light and making forbidden turns.

The particular geometry of panoramic cameras defines complex epipolar lines equations. In this paper, we present a way to warp images from a panoramic stereovision bench, so that the epipolar lines become parallel straight lines, thus allowing the use of an optimized fast pixel correlation based stereovision algorithm. The paper first introduces the geometric characterization of panoramic camera composed of parabolic and spherical mirrors, that computes both the intrinsic parameters of the system (mirror surfaces and intrinsic camera parameters) and the errors alignment between the mirrors. Then, it presents the warping equations that allow to generate rectified images. Calibration and stereovision results are presented.

In this paper, we present an approach to qualitative rover localization with panoramic images. The approach relies on the possibility to efficiently and robustly compute the resemblance between panoramic images, indexing them by histograms of local appearances. A database of image indexes is dynamically built during rover motions: when the rover re-perceives an already crossed area, it matches the current image with the stored ones (place recognition), and thus gets a qualitative estimate of its position. Experimental results on a 400 images database illustrates the effectiveness of the algorithms.