Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
A New Catadioptric Sensor for the Panoramic Vision of Mobile Robots
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Calibration of Panoramic Catadioptric Sensors Made Easier
OMNIVIS '02 Proceedings of the Third Workshop on Omnidirectional Vision
Omnidirectional Vision: Theory and Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Towards Complete Generic Camera Calibration
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Instantaneous robot self-localization and motion estimation with omnidirectional vision
Robotics and Autonomous Systems
Reconfigurable computing system for image processing via the internet
Microprocessors & Microsystems
Real-Time FPGA-Based Panoramic Unrolling of High-Resolution Catadioptric Omnidirectional Images
ICMTMA '09 Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
FPGA-based image processing for omnidirectional vision on mobile robots
Proceedings of the 24th symposium on Integrated circuits and systems design
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This work presents the development of an integrated hardware/software sensor system for moving object detection and distance calculation, based on background subtraction algorithm. The sensor comprises a catadioptric system composed by a camera and a convex mirror that reflects the environment to the camera from all directions, obtaining a panoramic view. The sensor is used as an omnidirectional vision system, allowing for localization and navigation tasks of mobile robots. Several image processing operations such as filtering, segmentation and morphology have been included in the processing architecture. For achieving distance measurement, an algorithm to determine the center of mass of a detected object was implemented. The overall architecture has been mapped onto a commercial low-cost FPGA device, using a hardware/software co-design approach, which comprises a Nios II embedded microprocessor and specific image processing blocks, which have been implemented in hardware. The background subtraction algorithm was also used to calibrate the system, allowing for accurate results. Synthesis results show that the system can achieve a throughput of 26.6 processed frames per second and the performance analysis pointed out that the overall architecture achieves a speedup factor of 13.78 in comparison with a PC-based solution running on the real-time operating system xPC Target.