Towards Next Best View Planning for Observation of Time-Variant Scenes

Abstract: Despite the inherent dynamic nature of real world environments, much robotics research focus on applying new concepts and techniques in isolated, static contexts. To further advance the field, it could be useful to explore not only how to compensate for dynamic features, but also how such features could be exploited. In this work, we investigate how next best view planning can be adapted for observation of dynamic scenes. To this purpose, we conduct a thorough review of di˙erent representations of information, based on prece-dent from the well-studied static case. It is found that view planning cannot be directly transferred from static environments to dynamic scenes without accounting for information decay and observational bias. The result is new insight to the time-variant view planning problem.