Active Exploration for Robust Object Detection
Name
Roy_Active Exploration.pdf
Size
3.77 MB
Format
Adobe PDF
Checksum (MD5)
2841870f81fd4e9f563c7a9076359430
Author(s)
Velez, Javier J.
Hemann, Garrett A.
Huang, Albert S.
Posner, Ingmar
Roy, Nicholas
Date Issued
July 2011
Journal
Twenty-Second International Joint Conference on Artificial Intelligence
Citation
Javier Velez, Garrett Hemann, Albert S. Huang, Ingmar Posner, and Nicholas Roy "Active Exploration for Robust Object Detection." Int. Joint Conf. on Artificial Intelligence (IJCAI), Barcelona, Spain, Jul. 2011
Version
Author's final manuscript
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 vantage 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://ijcai-11.iiia.csic.es/program/paper/37
