View Planning for Unknown Indoor Scenes Based on a Cost Benefit Analysis

From the task of automatically reconstructing real world scenes using range images, the problem of planning the image acquisition arises. Although solutions for small objects in known environments are already available, these approaches lack scalability to large scenes and to a high number of degrees of freedom. In this paper, we present a new planning algorithm for initially unknown, large indoor environments. Using a surface representation of seen and unseen parts of the environment, we propose a method based on the analysis of occlusions. In addition to previous approaches, we take into account both a quality criterion and the cost of the next acquisition. Results are shown for two large indoor scenes - an artificial scene and a real world room - with numerous self occlusions.