Active Exploration for Robust Object Detection

Abstract

Today, mobile robots are increasingly expected to operate in ever more complex and dynamic envi-ronments. In order to carry out many of the higher-level tasks envisioned a semantic understanding of a workspace is pivotal. Here our field has benefited significantly from successes in machine learning and vision: applications in robotics of off-the-shelf object detectors are plentiful. This paper outlines an online, any-time planning framework enabling the active exploration of such detections. Our ap-proach exploits the ability to move to different van-tage points and implicitly weighs the benefits of gaining more certainty about the existence of an object against the physical cost of the exploration required. The result is a robot which plans trajecto-ries specifically to decrease the entropy of putative detections. Our system is demonstrated to signif-icantly improve detection performance and trajec-tory length in simulated and real robot experiments.