Abstract – Today, mobile robots are increasingly expected to operate in ever more complex and dynamic environments. 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 approach 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 trajectories specifically to decrease the entropy of putative detections. Our system is demonstrated to significantly improve detection performance and trajectory length in simulated and real robot experiments.

  • [PDF] J. Velez, G. Hemann, A. S. Huang, I. Posner, and N. Roy, “Active Exploration for Robust Object Detection,” in International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011.
    Address = {Barcelona, Spain},
    Author = {Javier Velez and Garrett Hemann and Albert S. Huang and Ingmar Posner and Nicholas Roy},
    Booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
    Keywords = {conference_posner},
    Month = {July},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2011IJCAI_posner.pdf},
    Title = {Active Exploration for Robust Object Detection},
    Year = {2011}}