A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%.

[1]  K. Madhava Krishna,et al.  Markov Random Field based small obstacle discovery over images , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[2]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[3]  David Johnson,et al.  Radar Sensing for Intelligent Vehicles in Urban Environments , 2015, Sensors.

[4]  John K. Tsotsos,et al.  Region Classification for Robust Floor Detection in Indoor Environments , 2009, ICIAR.

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

[6]  Gunnar Farnebäck,et al.  Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

[7]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Guilherme N. DeSouza,et al.  Homography-based ground plane detection for mobile robot navigation using a Modified EM algorithm , 2010, 2010 IEEE International Conference on Robotics and Automation.

[9]  Heinrich Niemann,et al.  Integrated motion and geometry-based obstacle detection in image sequences of traffic scenes , 1996, Defense, Security, and Sensing.

[10]  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.

[11]  A. Hussain,et al.  Implementation of inverse perspective mapping algorithm for the development of an automatic lane tracking system , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[12]  Pedro Arias,et al.  3D Modeling of Building Indoor Spaces and Closed Doors from Imagery and Point Clouds , 2015, Sensors.

[13]  Jilin Liu,et al.  Monocular Vision Based Obstacle Detection for Robot Navigation in Unstructured Environment , 2007, ISNN.

[14]  Hyun Myung,et al.  Graph Structure-Based Simultaneous Localization and Mapping Using a Hybrid Method of 2D Laser Scan and Monocular Camera Image in Environments with Laser Scan Ambiguity , 2015, Sensors.

[15]  Alberto Olivares,et al.  Towards the Automatic Scanning of Indoors with Robots , 2015, Sensors.

[16]  Hui Wang,et al.  Real-time region-based obstacle detection with monocular vision , 2005, 2005 IEEE International Conference on Robotics and Biomimetics - ROBIO.

[17]  Stanley T. Birchfield,et al.  Image-based segmentation of indoor corridor floors for a mobile robot , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  G. Jiang,et al.  Lane and obstacle detection based on fast inverse perspective mapping algorithm , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[19]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Baoxin Li,et al.  Robust Ground Plane Detection with Normalized Homography in Monocular Sequences from a Robot Platform , 2006, 2006 International Conference on Image Processing.

[21]  Amnon Shashua,et al.  Vision-based ACC with a single camera: bounds on range and range rate accuracy , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[22]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Xuenan Cui,et al.  Floor segmentation by computing plane normals from image motion fields for visual navigation , 2009 .

[24]  Xavier Bresson,et al.  Fast Texture Segmentation Based on Semi-Local Region Descriptor and Active Contour , 2009 .

[25]  Ramani Duraiswami,et al.  The improved fast Gauss transform with applications to machine learning , 2005 .

[26]  Kahlouche Souhila,et al.  Optical Flow Based Robot Obstacle Avoidance , 2007 .

[27]  Kai-Tai Song,et al.  Robust ground plane detection for obstacle avoidance of mobile robots using a monocular camera , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Anton Kummert,et al.  Vision-based pedestrian detection -reliable pedestrian candidate detection by combining IPM and a 1D profile , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[29]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[30]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Robert Laganière,et al.  Single-view obstacle detection for smart back-up camera systems , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[32]  Zhongke Shi,et al.  A Novel Traffic Stream Detection Method Based on Inverse Perspective Mapping , 2012 .

[33]  K. Madhava Krishna,et al.  A Bayes filter based adaptive floor segmentation with homography and appearance cues , 2012, ICVGIP '12.

[34]  T. Naito,et al.  The Obstacle Detection Method using Optical Flow Estimation at the Edge Image , 2007, 2007 IEEE Intelligent Vehicles Symposium.

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