The Autonomous City Explorer Project: Towards Navigation by Interaction and Visual Perception

The Autonomous City Explorer (ACE) project has the goal, to develop a robot which is capable of finding its way to a given destination in an unknown urban environment. An exemplary mission is to find the way from our institute to the marienplatz, a public place in the center of munich, without any prior knowledge or gps information. Inspired by the behavior of humans in unknown environments, ACE must find its way by asking pedestrians. The distance of the route is about four kilometers and includes heavily traveled roads and crowded public places. In order to navigate safely in an unknown urban environment, some challenges arise for the vision system. Robust human detection, tracking and the estimation of human body poses is essential for natural interaction with pedestrians. Furthermore, the robot needs to be able to detect sidewalk and crossroads. A visual odometry system is used to support the conventional navigation. This paper describes both, an architecture of the vision system used for ACE and the algorithms used to deal with the described challenges.

[1]  Nicolas Courty,et al.  Visual perception based on salient features , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[2]  Qiong Liu,et al.  A robust skin color based face detection algorithm , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[3]  Wei Zou,et al.  Visual odometry based on locally planar ground assumption , 2005, 2005 IEEE International Conference on Information Acquisition.

[4]  Map Building from Human-Computer Interactions The Author Institution First line of institution , 2004 .

[5]  Illah R. Nourbakhsh,et al.  A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[6]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[7]  Dirk John,et al.  A mobile robot platform for assistance and entertainment , 2001 .

[8]  Andrew Price,et al.  Visual odometry for an outdoor mobile robot , 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..

[9]  Martin Buss,et al.  A multi-focal high-performance vision system , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[10]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[11]  Takayuki Kanda,et al.  Interactive Humanoid Robots for a Science Museum , 2006, IEEE Intelligent Systems.

[12]  Martin Buss,et al.  A model-based algorithm to estimate body poses using stereo vision , 2008, RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication.

[13]  SchieleBernt,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008 .

[14]  Illah R. Nourbakhsh,et al.  Appearance-Based Obstacle Detection with Monocular Color Vision , 2000, AAAI/IAAI.

[15]  Vladimir Vezhnevets,et al.  A Survey on Pixel-Based Skin Color Detection Techniques , 2003 .

[16]  Christian Dornhege,et al.  Visual Odometry for Tracked Vehicles , 2006 .

[17]  Illah R. Nourbakhsh,et al.  The mobot museum robot installations: a five year experiment , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[18]  Artur M. Arsénio,et al.  Map Building from Human-Computer Interactions , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[19]  Rüdiger Dillmann,et al.  Sensor fusion for 3D human body tracking with an articulated 3D body model , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[20]  Seong-Whan Lee,et al.  Reconstructing 3D Human Body Pose from Stereo Image Sequences Using Hierarchical Human Body Model Learning , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[21]  Kolja Kuhnlenz,et al.  Aspects of Multi-Focal Vision , 2007 .

[22]  Luca Iocchi,et al.  Human Posture Tracking and Classification through Stereo Vision and 3D Model Matching , 2008, EURASIP J. Image Video Process..

[23]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[24]  Sven Wachsmuth,et al.  Active Vision-based Localization For Robots In A Home-Tour Scenario , 2007, ICVS 2007.