Not seeing is also believing: Combining object and metric spatial information

Spatial representations are fundamental to mobile robots operating in uncertain environments. Two frequently-used representations are occupancy grid maps, which only model metric information, and object-based world models, which only model object attributes. Many tasks represent space in just one of these two ways; however, because objects must be physically grounded in metric space, these two distinct layers of representation are fundamentally linked. We develop an approach that maintains these two sources of spatial information separately, and combines them on demand. We illustrate the utility and necessity of combining such information through applying our approach to a collection of motivating examples.

[1]  Wolfram Burgard,et al.  Hierarchies of octrees for efficient 3D mapping , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Danica Kragic,et al.  Object detection and mapping for service robot tasks , 2007, Robotica.

[3]  Bhaskara Marthi,et al.  An object-based semantic world model for long-term change detection and semantic querying , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[5]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[6]  Ziyuan Liu,et al.  Extracting semantic indoor maps from occupancy grids , 2014, Robotics Auton. Syst..

[7]  Patric Jensfelt,et al.  Large-scale semantic mapping and reasoning with heterogeneous modalities , 2012, 2012 IEEE International Conference on Robotics and Automation.

[8]  Kurt Konolige,et al.  Navigation in hybrid metric-topological maps , 2011, 2011 IEEE International Conference on Robotics and Automation.

[9]  Jos Elfring,et al.  Semantic world modeling using probabilistic multiple hypothesis anchoring , 2013, Robotics Auton. Syst..

[10]  Leslie Pack Kaelbling,et al.  Data association for semantic world modeling from partial views , 2015, Int. J. Robotics Res..

[11]  Benjamin Kuipers,et al.  The Spatial Semantic Hierarchy , 2000, Artif. Intell..

[12]  Gregory D. Hager,et al.  Scene parsing using a prior world model , 2011, Int. J. Robotics Res..

[13]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[14]  Leslie Pack Kaelbling,et al.  Collision-free state estimation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[15]  Frank Dellaert,et al.  Semantic Modeling of Places using Objects , 2007, Robotics: Science and Systems.

[16]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .