Hybrid field of view vision: From biological inspirations to integrated sensor design

In this article a hybrid field of view vision system employing both a catadioptric camera and a typical perspective-view camera is presented and evaluated. This configuration takes biological inspirations from the peripheral and foveal vision co-operation in animals and compensates the most important drawbacks of both camera types. To make the sensor self-contained and suitable for mobile robots lacking a powerful on-board controller the design is based on a recent generation single-board computer providing parallel processing by means of GPGPU. The software of our sensor permits to use it for quick detection of obstacles in the vicinity of the robot, and to measure distances using stereo vision.

[1]  Davide Scaramuzza,et al.  Omnidirectional Vision: From Calibration to Root Motion Estimation , 2007 .

[2]  Roland Siegwart,et al.  A Toolbox for Easily Calibrating Omnidirectional Cameras , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[4]  Alexandru Tupan,et al.  Triangulation , 1997, Comput. Vis. Image Underst..

[5]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[6]  Piotr Skrzypczyński,et al.  Improving Self-Localization Efficiency in a Small Mobile Robot by Using a Hybrid Field of View Vision System , 2015 .

[7]  Stefano Cagnoni,et al.  Hybrid Stereo Sensor with Omnidirectional Vision Capabilities: Overview and Calibration Procedures , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[8]  Paul Levi 3D Object Localization via Stereo Vision using an Omnidirectional and a Perspective Camera , 2009 .

[9]  Sing Bing Kang,et al.  Catadioptric self-calibration , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  Giulio Sandini,et al.  Divergent stereo in autonomous navigation: From bees to robots , 1995, International Journal of Computer Vision.

[11]  H. Bakstein,et al.  Panoramic mosaicing with a 180/spl deg/ field of view lens , 2002, Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02.

[12]  Stefano Cagnoni,et al.  A Non-traditional Omnidirectional Vision System with Stereo Capabilities for Autonomous Robots , 2001, AI*IA.

[13]  Marta Rostkowska,et al.  Embedded, GPU-based omnidirectional vision for a walking robot , 2017 .

[14]  Andrzej Kasiński,et al.  Perception network for the team of indoor mobile robots: concept, architecture, implementation , 2001 .

[15]  Emanuele Menegatti,et al.  Cooperation between Omnidirectional Vision Agents and Perspective Vision Agents for Mobile Robots , 2002 .

[16]  Gordon Cheng,et al.  Foveated vision systems with two cameras per eye , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[17]  Huei-Yung Lin,et al.  HOPIS: Hybrid Omnidirectional and Perspective Imaging System for Mobile Robots , 2014, Sensors.

[18]  Laurent Itti,et al.  Biologically Inspired Mobile Robot Vision Localization , 2009, IEEE Transactions on Robotics.