Active Object Segmentation for Mobile Robots

The goal of active vision is to change intrinsic or extrinsic properties of the sensor in order to get new and improved information. In the case of 3-D object modeling from vision, this can mean moving the camera to view the scene from a new angle or to get a close-up view of an object that has been localized and is being modeled. We discuss using active vision to improve the speed and utility of map completion and object segmentation. Importantly, in order to be able to process untextured surfaces, we avoid relying on the existence of distinctive visual point features.

[1]  C. Ian Connolly,et al.  The determination of next best views , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[2]  Hugh F. Durrant-Whyte,et al.  Consistent Integration and Propagation of Disparate Sensor Observations , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[3]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[4]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[5]  R. Bajcsy.  Active perception , 1988, Proc. IEEE.

[6]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[7]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[8]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[9]  Héctor H. González-Baños,et al.  Navigation Strategies for Exploring Indoor Environments , 2002, Int. J. Robotics Res..

[10]  W. Burgard,et al.  First steps towards a robotic system for flexible volumetric mapping of indoor environments , 2004, IFAC Proceedings Volumes.

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

[12]  Karsten Berns,et al.  Dynamic Frontier Based Exploration with a Mobile Indoor Robot , 2010, ISR/ROBOTIK.

[13]  Sven Behnke,et al.  Evaluating the Efficiency of Frontier-based Exploration Strategies , 2010, ISR/ROBOTIK.

[14]  Takeo Kanade,et al.  Automated Construction of Robotic Manipulation Programs , 2010 .

[15]  Dieter Fox,et al.  Interactive 3D modeling of indoor environments with a consumer depth camera , 2011, UbiComp '11.

[16]  Andreas Krause,et al.  Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization , 2010, J. Artif. Intell. Res..

[17]  Hadas Kress-Gazit.  Robot challenges: Toward development of verification and synthesis techniques [from the Guest Editors] , 2011 .

[18]  Markus Vincze,et al.  Knowing your limits - self-evaluation and prediction in object recognition , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Dieter Fox,et al.  Toward object discovery and modeling via 3-D scene comparison , 2011, 2011 IEEE International Conference on Robotics and Automation.

[20]  Nico Blodow,et al.  Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Dieter Fox,et al.  Autonomous generation of complete 3D object models using next best view manipulation planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[22]  Dieter Fox,et al.  RGB-D object discovery via multi-scene analysis , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Patric Jensfelt,et al.  Exploiting and modeling local 3D structure for predicting object locations , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Maren Bennewitz,et al.  Navigation in three-dimensional cluttered environments for mobile manipulation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[25]  Geoffrey A. Hollinger,et al.  Uncertainty-driven view planning for underwater inspection , 2012, 2012 IEEE International Conference on Robotics and Automation.

[26]  Vijay Kumar,et al.  Stochastic differential equation-based exploration algorithm for autonomous indoor 3D exploration with a micro-aerial vehicle , 2012, Int. J. Robotics Res..

[27]  D. Fox,et al.  Object Segmentation from Motion with Dense Feature Matching , 2012 .

[28]  Siddhartha S. Srinivasa,et al.  Efficient touch based localization through submodularity , 2012, 2013 IEEE International Conference on Robotics and Automation.

[29]  Dieter Fox,et al.  RGB-D flow: Dense 3-D motion estimation using color and depth , 2013, 2013 IEEE International Conference on Robotics and Automation.

[30]  Dieter Fox,et al.  Patch Volumes: Segmentation-Based Consistent Mapping with RGB-D Cameras , 2013, 2013 International Conference on 3D Vision.

[31]  George J. Pappas,et al.  Hypothesis testing framework for active object detection , 2013, 2013 IEEE International Conference on Robotics and Automation.

[32]  Siddhartha S. Srinivasa,et al.  CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..

[33]  Gaurav S. Sukhatme,et al.  A probabilistic framework for next best view estimation in a cluttered environment , 2014, J. Vis. Commun. Image Represent..

[34]  Gearing Up and Accelerating Cross-fertilization between Academic and Industrial Robotics Research in Europe: - Technology Transfer Experiments from the ECHORD Project , 2014, Springer Tracts in Advanced Robotics.

[35]  Jun Li,et al.  Active Recognition and Manipulation for Mobile Robot Bin Picking , 2014, Technology Transfer Experiments from the ECHORD Project.

[36]  Dieter Fox,et al.  Toward online 3-D object segmentation and mapping , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).