Learning Effective Sparse Sampling Strategies using Deep Active Sensing

Mehdi Stapleton, Dieter Schmalstieg, Clemens Arth, Thomas Gloor

Abstract: Registering a known model with noisy sample measurements is in general a difficult task due to the problem in finding correspondences between the samples and points on the known model. General frameworks exist, such as variants of the classical iterative closest point (ICP) method to iteratively refine correspondence estimates. However, the methods are prone to getting trapped in locally optimal configurations, which may be far from the true registration. The quality of the final registration depends strongly on the set of samples. The quality of the set of sample measurements is more noticeable when the number of samples is relatively low (≈ 20). We consider sample selection in the context of active perception, i.e. an objective-driven decision-making process, to motivate our research and the construction of our system. We design a system for learning how to select the regions of the scene to sample, and, in doing so, improve the accuracy and efficiency of the sampling proc