Real-Time Simulation of Camera Errors and Their Effect on Some Basic Robotic Vision Algorithms

We present a real-time approximate simulation of some camera errors and the effects these errors have on some common computer vision algorithms for robots. The simulation uses a software framework for real-time post processing of image data. We analyse the performance of some basic algorithms for robotic vision when adding modifications to images due to camera errors. The result of each algorithm / error combination is presented. This simulation is useful to tune robotic algorithms to make them more robust to imperfections of real cameras.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Xiaodong Yang,et al.  Robust door detection in unfamiliar environments by combining edge and corner features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[3]  J. Roßmann,et al.  VALIDATING THE CAMERA AND LIGHT SIMULATION OF A VIRTUAL SPACE ROBOTICS TESTBED BY MEANS OF PHYSICAL MOCKUP DATA , 2012 .

[4]  André Hinkenjann,et al.  GrIP: A Framework for Experiments with Screen Space Algorithms , 2011 .

[5]  Fukui Kazuhiro,et al.  Realistic CG Stereo Image Dataset With Ground Truth Disparity Maps , 2012 .

[6]  S. Khan,et al.  Real time lane detection for autonomous vehicles , 2008, 2008 International Conference on Computer and Communication Engineering.

[7]  Pavel Zemčı́k Simulation of Camera Features , 2012 .

[8]  Bernhard Wirnitzer,et al.  Improving feature based object recognition in service robotics by disparity map based segmentation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Atsuto Maki,et al.  Towards a simulation driven stereo vision system , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[10]  Jürgen Roßmann,et al.  A Real-Time Optical Sensor Simulation Framework for Development and Testing of Industrial and Mobile Robot Applications , 2012, ROBOTIK.

[11]  Tamio Arai,et al.  Development of a Simulator of Environment and Measurement for Autonomous Mobile Robots Considering Camera Characteristics , 2003, RoboCup.

[12]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[13]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[14]  Hans-Peter Seidel,et al.  An Image-Based Model for Realistic Lens Systems in Interactive Computer Graphics , 1997, Graphics Interface.

[15]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[16]  Christian A. Mueller,et al.  Towards Robust Object Categorization for Mobile Robots with Combination of Classifiers , 2012, RoboCup.

[17]  Darius Burschka,et al.  Stereo-based obstacle avoidance in indoor environments with active sensor re-calibration , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).