A theoretical model, representing the sensor based sheet for machines, in object detection/perception

In this paper, we have introduced the framework of a virtual theoretical model representing, combination of a number of different overcrowded sensors mainly consist of commonly used sensors e.g. ultra sonic, Infra red and laser range finders etc, on a designed sheet; for algorithm testing and development in human machine interaction based academic research. By using a number of different sensors with different combinations, we can get an idea about the environment in front of that sheet, and by using softwares, we can turn the feedback given by the sensors to an image, or even know certain sets of information about the objects placed in front of it. We can also get a slightly better idea about the challenging part in perception based design, that is, edge detection. This paper also provides a detailed possible applications part for this sensor-based sheet with a major example on Futuristic feedback-giving skin in machines like robots etc, and the way it can work as an additional tool for the computer sciences along with the previously held computer vision tool. The effectiveness of the work is appreciated throughout the explanation segments with concentration on real as well as virtual world mapping. As an effective possible addition to vision-based systems, this paper also discuss the impact of energy losses; as a drawback, due to the noise addition to the system, with the major reason as the use of sensors in a bigger numbers. Similar nature problems are discussed in the final segments.

[1]  Sidney S. Fels,et al.  A Conceptual Structure for Computer Vision , 2011, 2011 Canadian Conference on Computer and Robot Vision.

[2]  Mario Chacon,et al.  Data processing from a Laser Range Finder sensor for the construction of geometric maps of an indoor environment , 2009, 2009 52nd IEEE International Midwest Symposium on Circuits and Systems.

[3]  Kostas J. Kyriakopoulos,et al.  Simultaneous localization and map building for mobile robot navigation , 1999, IEEE Robotics Autom. Mag..

[4]  Dieter Fox,et al.  RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments , 2010, ISER.

[5]  Lindsay Kleeman,et al.  Mobile Robot Sonar for Target Localization and Classification , 1995, Int. J. Robotics Res..

[6]  D. C. Lee.  The Map-Building and Exploration Strategies of A Simple Sonar-Equipped Robot: Bibliography , 1996 .

[7]  Yu-Cheol Lee,et al.  Sonar Grid Map Based Localization for Autonomous Mobile Robots , 2008, 2008 IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications.

[8]  Li Zhang,et al.  Line segment based map building and localization using 2D laser rangefinder , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Wolfram Burgard,et al.  G2o: A general framework for graph optimization , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  R. Langari,et al.  Line map construction using a mobile robot with a sonar sensor , 2005, Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics..

[11]  Ulrich Raschke,et al.  A comparison of grid-type map-building techniques by index of performance , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[12]  G. Oriolo,et al.  On-line map building and navigation for autonomous mobile robots , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[13]  Frank Dellaert,et al.  iSAM: Incremental Smoothing and Mapping , 2008, IEEE Transactions on Robotics.

[14]  Roman Kuc,et al.  Single sensor sonar map-building based on physical principles of reflection , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[15]  Tom Duckett,et al.  A multilevel relaxation algorithm for simultaneous localization and mapping , 2005, IEEE Transactions on Robotics.

[16]  Vassilis Varveropoulos,et al.  Robot Localization and Map Construction Using Sonar Data , 2001 .

[18]  Tao Mei,et al.  The design and fabrication of a flexible three-dimensional force sensor skin , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  N. M. Arshad,et al.  Construction sonar sensor model of low altitude field mapping sensors for application on a UAV , 2012, 2012 IEEE 8th International Colloquium on Signal Processing and its Applications.

[20]  John J. Leonard,et al.  Adaptive concurrent mapping and localization using sonar , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[21]  Doik Kim,et al.  Detachable tactile sensor skin module for robotic applications , 2013, 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[22]  Hiroshi Ishiguro,et al.  Automatic 2D map construction using a special catadioptric sensor , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.