Observation planning for efficient environment information summarization

Mapping is an activity of making a useful description of an environment. Not only geometric information such as free space but also object placements are important if the map is used for human-robot communication. We call such a map making environment information summarization because how to summarize may change depending on the purpose of the map. Environment information summarization usually includes searching for specified objects in the environment. It is, therefore, crucial to make a good observation plan for efficient summarization. We develop an observation planning method which uses object appearance models for appropriately handling a trade-off between visual data quality and vision cost. Experimental results using a vision-based humanoid robot show the effectiveness of the proposed planning method.

[1]  Ramesh Krishnamurti,et al.  View Planning Problem with Combined View and Traveling Cost , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[2]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[3]  Masahiro Tomono,et al.  3-D Object Map Building Using Dense Object Models with SIFT-based Recognition Features , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Vivek A. Sujan,et al.  Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents , 2005, J. Intell. Robotic Syst..

[5]  Hugh F. Durrant-Whyte,et al.  Simultaneous map building and localization for an autonomous mobile robot , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[6]  Jun Miura,et al.  Vision Planning for Object Search using Multiple Visual Features , 2008 .

[7]  Olivier Stasse,et al.  Online object search with a humanoid robot , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Yoshiaki Shirai,et al.  Vision and Motion Planning for a Mobile Robot under Uncertainty , 1997, Int. J. Robotics Res..

[9]  Alexei Makarenko,et al.  An experiment in integrated exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Hiroshi Murase,et al.  Image retrieval using efficient local-area matching , 1998, Machine Vision and Applications.

[11]  Roland Siegwart,et al.  Cognitive maps for mobile robots - an object based approach , 2007, Robotics Auton. Syst..

[12]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[13]  Yiming Ye,et al.  Sensor Planning for 3D Object Search, , 1999, Comput. Vis. Image Underst..

[14]  Ben J. A. Kröse,et al.  From images to rooms , 2007, Robotics Auton. Syst..

[15]  John K. Tsotsos,et al.  Visual search for an object in a 3D environment using a mobile robot , 2010, Comput. Vis. Image Underst..

[16]  Wolfram Burgard,et al.  Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  Cipriano Galindo,et al.  Multi-hierarchical semantic maps for mobile robotics , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.