Automatic sensor placement for volumetric object characterization

Active sensing is the process of exploring the environment using multiple views of a scene captured by sensors from different points in space under different sensor settings. Active sensing can be used for the modeling of unknown objects or the recognition of objects in a scene. Applications of active sensing are numerous and can be found in the medical field, in archeology, in the movie and advertisement industry, in manufacturing, and in the environmental industry. In this work, the focus is on the use of a single vision sensor (camera) to perform the volumetric modeling of an unknown object in an entirely autonomous fashion. The camera moves to acquire the necessary information in two ways: (a) viewing closely each local feature of interest using 2-D data; and (b) acquiring global information about the environment via 3-D sensor locations and orientations. An iterative 2-D optimization process is developed and the enhanced image at each step is projected along the corresponding viewing direction. The new projection is intersected with previously obtained projections for volume reconstruction. During the global exploration of the scene, the current image as well as previous images are used to maximize the information in terms of shape irregularity as well as contrast variations. The scene on the borders of occlusion (contours) is modeled by partitioning the contour images and evaluating an entropy-based objective functional on each contour segment. This functional is optimized to determine the best next view, which is recovered by computing the pose of the camera. A criterion based on the minimization of the difference between consecutive volume updates is set for termination of the exploration procedure. These steps are integrated into the design of an off-line Autonomous Model Construction System AMCS, based on data-driven active sensing. The system operates autonomously with no human intervention and with no prior knowledge about the object.