Data Driven Selective Sensing for 3D Image Acquisition

Abstract

It is well established that acquiring large amounts of range data with vision sensors can quickly lead to important data management challenges where processing capabilities become saturated and pre-empt full usage of the information available for autonomous systems to make educated decisions. While sub-sampling offers a naïve solution for reducing dataset dimension after acquisition, it does not capitalize on the knowledge available in already acquired data to selectively and dynamically drive the acquisition process over the most significant regions in a scene, the latter being generally characterized by variations in depth and surface shape in the context of 3D imaging. This thesis discusses the development of two formal improvement measures, the first based upon surface meshes and Ordinary Kriging that focuses on improving scene accuracy, and the second based upon probabilistic occupancy grids that focuses on improving scene coverage. Furthermore, three selection processes to automatically choose which locations within the field of view of a range sensor to acquire next are proposed based upon the two formal improvement measures. The first two selection processes each use only one of the proposed improvement measures. The third selection process combines both improvement measures in order to counterbalance the parameters of the accuracy of knowledge about the scene and the coverage of the scene. The proposed algorithms mainly target applications using random access range sensors, defined as sensors that can acquire depth measurements at a specified location within their field of view. Additionally, the algorithms are applicable to the case of estimating the improvement and point selection from within a single point of view, with the purpose of guiding the random access sensor to locations it can acquire. However, the framework is developed to be independent of the range sensing technology used, and is validated with range data of several scenes acquired from many different sensors employing various sensing technologies and configurations. Furthermore, the experimental results of the proposed selection processes are compared against those produced by a random sampling process, as well as a neural gas selective sensing algorithm.