Map building using occupancy grids with differentiated occupancy states

For map building purposes it is necessary to distinguish between moving and not moving objects, because moving objects might falsify map matching procedures. This paper presents a new approach of a grid based map building algorithm which represents an extension of occupancy grids. As well as it its archetype, the algorithm can easily be used for 2d or 3d time-dependent map building procedures. The original occupancy grids were extended so that cells which are marked as occupied get further differentiated regarding the motion characteristic of their particular obstacle. An occupied cell can therefore be differentiated in a cell occupied by a dynamic, quasi static or static obstacle. This differentiation might also come in handy regarding object detection or similar algorithms. The presented algorithm was developed and tested using a SICK LMS511 high resolution laser measurement system.

[1]  Franz Korf,et al.  Object tracking and dynamic estimation on evidential grids , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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

[3]  Luiz M. G. Gonçalves,et al.  3D Probabilistic Occupancy Grid to Robotic Mapping with Stereo Vision , 2012 .

[4]  Sergiu Nedevschi,et al.  Real-time environment representation based on Occupancy Grid temporal analysis using a Dense Stereo-Vision System , 2010, Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing.

[5]  Sergiu Nedevschi,et al.  Global map building based on occupancy grids detected from dense stereo in urban environments , 2009, 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing.

[6]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

[7]  Wolfram Burgard,et al.  Occupancy Grid Models for Robot Mapping in Changing Environments , 2012, AAAI.

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

[9]  Sergiu Nedevschi,et al.  Environment perception using dynamic polylines and particle based occupancy grids , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[10]  Radu Danescu.  Obstacle Detection Using Dynamic Particle-Based Occupancy Grids , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[11]  A. Elfes,et al.  Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception , 2013, ArXiv.

[12]  Alberto Elfes,et al.  Sensor integration for robot navigation: Combining sonar and stereo range data in a grid-based representataion , 1987, 26th IEEE Conference on Decision and Control.

[13]  Jizhong Xiao,et al.  Multi-volume occupancy grids: An efficient probabilistic 3D mapping model for micro aerial vehicles , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[15]  S. Sivanandam,et al.  Introduction to Fuzzy Logic using MATLAB , 2006 .

[16]  Kwang Hyung Lee,et al.  First Course on Fuzzy Theory and Applications , 2005, Advances in Soft Computing.

[17]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[18]  Alberto Elfes,et al.  A tesselated probabilistic representation for spatial robot perception and navigation , 1989 .

[19]  Christian Laugier,et al.  Wavelet Occupancy Grids: A Method for Compact Map Building , 2005, FSR.

[20]  Alberto Elfes,et al.  Robot Navigation: Integrating Perception, Environmental Constraints and Task Execution Within a Probabilistic Framework , 1995, Reasoning with Uncertainty in Robotics.