Light-Weight Object Detection and Decision Making via Approximate Computing in Resource-Constrained Mobile Robots

Most of the current solutions for autonomous flights in indoor environments rely on purely geometric maps (e.g., point clouds). There has been, however, a growing interest in supplementing such maps with semantic information (e.g., object detections) using computer vision algorithms. Unfortunately, there is a disconnect between the relatively heavy computational requirements of these computer vision solutions, and the limited computation capacity available on mobile autonomous platforms. In this paper, we propose to bridge this gap with a novel Markov Decision Process framework that adapts the parameters of the vision algorithms to the incoming video data rather than fixing them a priori. As a concrete example, we test our framework on a object detection and tracking task, showing significant benefits in terms of energy consumption without considerable loss in accuracy, using a combination of publicly available and novel datasets.