Robot Evolutionary Localization Based on Attentive Visual Short-Term Memory

Cameras are one of the most relevant sensors in autonomous robots. However, two of their challenges are to extract useful information from captured images, and to manage the small field of view of regular cameras. This paper proposes implementing a dynamic visual memory to store the information gathered from a moving camera on board a robot, followed by an attention system to choose where to look with this mobile camera, and a visual localization algorithm that incorporates this visual memory. The visual memory is a collection of relevant task-oriented objects and 3D segments, and its scope is wider than the current camera field of view. The attention module takes into account the need to reobserve objects in the visual memory and the need to explore new areas. The visual memory is useful also in localization tasks, as it provides more information about robot surroundings than the current instantaneous image. This visual system is intended as underlying technology for service robot applications in real people's homes. Several experiments have been carried out, both with simulated and real Pioneer and Nao robots, to validate the system and each of its components in office scenarios.

[1]  Mandyam V. Srinivasan,et al.  An Optical System for Guidance of Terrain Following in UAVs , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[2]  A. Aguado,et al.  Incremental map building using an occupancy grid for an autonomous monocular robot , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[3]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[4]  Wolfram Burgard,et al.  Active mobile robot localization by entropy minimization , 1997, Proceedings Second EUROMICRO Workshop on Advanced Mobile Robots.

[5]  Larry Matthies,et al.  Stereo vision and rover navigation software for planetary exploration , 2002, Proceedings, IEEE Aerospace Conference.

[6]  Max Q.-H. Meng,et al.  Genetic Algorithm Based Visual Localization for a Robot PET in Home Healthcare System , 2007, Int. J. Inf. Acquis..

[7]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[8]  Dario Floreano,et al.  Active vision and feature selection in evolutionary behavioral systems , 2002 .

[9]  Mark Lee,et al.  Implementing inhibition of return: embodied visual memory for robotic systems , 2009 .

[10]  Dana H. Ballard,et al.  Animate Vision , 1991, Artif. Intell..

[11]  Frank P. Ferrie,et al.  Entropy-based gaze planning , 2001, Image Vis. Comput..

[12]  Patrick Gros,et al.  3D navigation based on a visual memory , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[13]  Patric Jensfelt,et al.  Active global localization for a mobile robot using multiple hypothesis tracking , 2001, IEEE Trans. Robotics Autom..

[14]  Stergios I. Roumeliotis,et al.  Active vision-based robot localization and navigation in a visual memory , 2011, 2011 IEEE International Conference on Robotics and Automation.

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

[16]  Bruce A. Draper,et al.  A practical obstacle detection and avoidance system , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[17]  Andrew J. Davison,et al.  Live dense reconstruction with a single moving camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  J.M. Canas,et al.  Overt visual attention inside JDE control architecture , 2005, 2005 portuguese conference on artificial intelligence.

[19]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[20]  Tom Duckett.  A genetic algorithm for simultaneous localization and mapping , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[21]  Luis Moreno,et al.  A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors , 1999, J. Intell. Robotic Syst..

[22]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[23]  John K. Tsotsos,et al.  Towards a Biologically Plausible Active Visual Search Model , 2004, WAPCV.

[24]  Wolfram Burgard,et al.  Using the CONDENSATION algorithm for robust, vision-based mobile robot localization , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[25]  Andrew J. Davison,et al.  Lightweight SLAM and Navigation with a Multi-Camera Rig , 2011, ECMR.

[26]  Amiya Nayak,et al.  Robust line extraction based on repeated segment directions on image contours , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[27]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.