Development of a visual object localization module for mobile robots

Reports preliminary results from the design and implementation of a visual object localization module for mobile robots. The module takes an object-based approach to visual processing and relies on a preprocessing step that segments objects from the image. By tracking the size and the eccentricity of the objects in the image while the robot is moving, the visual object localization module can determine the position of objects relative to the robot using the displacement obtained from its odometry. In localizing the objects, the module is designed to combine the results of two different techniques. The visual looming technique measures the distance to an object using the change in the size of the object on the image plane. This technique is to be complemented by a variant of the triangulation technique that can locate an object using the eccentricity of the object when viewed from two different points. The module-with the preprocessing algorithm-is being implemented to run in real-time on a mobile robot. Evaluation of the visual localization module is being done in an integrated system introduced in the article. The integrated system creates an environment for real-time evaluation of the module as well as other mapping and navigation algorithms for mobile robots.

[1]  Mandyam V. Srinivasan,et al.  Robot Navigation by Visual Dead-Reckoning Inspiration From Insects , 1997, Int. J. Pattern Recognit. Artif. Intell..

[2]  Frank V. Koss,et al.  Comprehensive study of three-object triangulation , 1993, Other Conferences.

[3]  Nicola Ancona,et al.  Optical flow from 1-D correlation: Application to a simple time-to-crash detector , 1993, 1993 (4th) International Conference on Computer Vision.

[4]  Liqiang Feng,et al.  Navigating Mobile Robots: Systems and Techniques , 1996 .

[5]  Michael P. Case.  Single Landmark Navigation By Mobile Robots , 1987, Other Conferences.

[6]  Erol Sahin,et al.  Mobile robot range sensing through visual looming , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[7]  Daniel Raviv,et al.  Autonomous obstacle avoidance using visual fixation and looming , 1992, Other Conferences.

[8]  Daniel Raviv.  Visual looming , 1992, Other Conferences.

[9]  S. B. Xu.  Qualitative depth from monoscopic cues , 1992 .

[10]  Ian Horswill.  Collision Avoidance by Segmentation , 1995 .

[11]  Daniel P. Huttenlocher,et al.  Visually-guided navigation by comparing edge images , 1995 .

[12]  P. Cz..  Handbuch der physiologischen Optik , 1896 .

[13]  Ian Horswill.  Visual collision avoidance by segmentation , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[14]  Rodney A. Brooks,et al.  Visually-guided obstacle avoidance in unstructured environments , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[15]  Allen R. Hanson,et al.  Translating Optical Flow Into Token Matches And Depth From Looming , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[16]  Karen Roberts,et al.  'Bee-bot': using peripheral optical flow to avoid obstacles , 1992, Other Conferences.

[17]  James A. Caviness,et al.  Persistent Fear Responses in Rhesus Monkeys to the Optical Stimulus of "Looming" , 1962, Science.

[18]  Ronald D. Chaney.  Multiple-frame motion estimation using simple region features , 1994, Other Conferences.