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2012 | 22 | 2 | 409-421
Tytuł artykułu

Straight-lines modelling using planar information for monocular SLAM

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This work proposes a SLAM (Simultaneous Localization And Mapping) solution based on an Extended Kalman Filter (EKF) in order to enable a robot to navigate along the environment using information from odometry and pre-existing lines on the floor. These lines are recognized by a Hough transform and are mapped into world measurements using a homography matrix. The prediction phase of the EKF is developed using an odometry model of the robot, and the updating makes use of the line parameters in Kalman equations without any intermediate stage for calculating the distance or the position. We show two experiments (indoor and outdoor) dealing with a real robot in order to validate the project.
Słowa kluczowe
Rocznik
Tom
22
Numer
2
Strony
409-421
Opis fizyczny
Daty
wydano
2012
otrzymano
2011-01-05
poprawiono
2011-08-23
Twórcy
  • Department of Informatics and Statistics, Federal University of Piauí, Teresina, PI, 64049-550, Brasil
  • Department of Informatics and Statistics, Federal University of Piauí, Teresina, PI, 64049-550, Brasil
  • Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brasil
Bibliografia
  • Ahn, S., Chung, W.K. and Oh, S. (2007). Construction of hybrid visual map for indoor SLAM, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Diego, CA, USA, pp. 1695-1701.
  • Amarasinghe, D., Mann, G. and Gosine, R. (2009). Landmark detection and localization for mobile robot applications: A multisensor approach, Robotica Cambridge 28(5): 663-673.
  • Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly Media, Sebastopol, CA.
  • Calway, A. and Cuevas, W. (2008). Discovering higher level structure in visual SLAM, IEEE Transactions on Robotics 24(5): 980-990.
  • Canny, J. (1986). A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6): 679-698.
  • Chen, Z. and Samarabandu, J. (2006). A visual SLAM solution based on high level geometry knowledge and Kalman filtering, Canadian Conference on Electrical and Computer Engineering (CCECE), Ottawa, Canada, pp. 1283-1286.
  • Choi, J., Ahn, S., Choi, M. and Chung, W. (2006). Metric SLAM in home environment with visual objects and sonar features, International Conference on Intelligent Robots and Systems (IROS), Beijing, China, pp. 4048-4053.
  • Civera, J., Davison, A.J. and Montiel, J.M. (2008). Inverse depth parametrization for monocular SLAM, IEEE Transactions on Robotics 24(5): 932-945.
  • Clemente, L., Davison, A., Reid, I., Neira, J. and Tardos, J. (2007). Mapping large loops with a single hand-held camera, Robotics: Science and Systems, Atlanta, GA, USA.
  • Dailey, M. and Parnichkun, M. (2005). Landmark based simultaneous localization and mapping with stereo vision, Asian Conference on Industrial Automation and Robotics (ACIAR), Bangkok, Thailand, pp. 108-113.
  • Dao, N., You, B., Oh, S. and Hwangbo, M. (2003). Visual selflocalization for indoor mobile robots using natural lines, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, pp. 1252-1257.
  • Davison, A.J., Cid, Y.G. and Kita, N. (2004). Real-time 3D SLAM with wide-angle vision, Symposium on Intelligent Autonomous Vehicles (IAV), Lisbon, Portugal.
  • Davison, A.J. and Murray, D.W. (2002). Simultaneous localization and map-building using active vision, IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7): 865-880.
  • Durrant-Whyte, H. and Bailey, T. (2006a). Simultaneous localization and mapping: Part I, IEEE Transactions on Robotics and Automation 13(2): 99-108.
  • Durrant-Whyte, H. and Bailey, T. (2006b). Simultaneous localization and mapping: Part II, IEEE Transactions on Robotics and Automation 13(3): 109-117.
  • Eade, E. and Drummond, T. (2006). Edge landmarks in monocular SLAM, British Machine Vision Conference (BMVC), Edinburgh, UK, pp. 7-17.
  • Estrada, C., Neira, J. and Tards, J.D. (2005). Hierarchical SLAM: Real-time accurate mapping of large environments, IEEE Transactions on Robotics 21(4): 588-596.
  • Forsyth, D. and Ponce, J. (2002). Computer Vision: A Modern Approach, Prentice Hall.
  • Frintrop, S., Jensfelt, P. and Christensen, H.I. (2006). Attentional landmark selection for visual SLAM, International Conference on Intelligent Robots and Systems (IROS).
  • Fu, S., Liu, H., Gao, L. and Gai, Y. (2007). SLAM for mobile robots using laser range finder and monocular vision, IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy, pp. 91-96.
  • Fu, S. and Yang, G. (2009). Uncalibrated monocular based SLAM for indoor autonomous mobile robot navigation, International Conference on Networking, Sensing and Control (ICNSC), Porto, Portugal, pp. 663-668.
  • Gonzalez, R. and Woodes, R. (2007). Digital Image Processing, Prentice Hall, Upper Saddle River, NJ.
  • Hafez, A., Bhuvanagiri, S., Krishna, M. and Jawahar, C. (2008). On-line convex optimization based solution for mapping in VSLAM, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice, France, pp. 4072-4077.
  • Herath, D.C., Kodagoda, K.R.S. and Dissanayake, G. (2007). Stereo vision based SLAM issues and solutions, IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy, pp. 1892-1897.
  • Hough, P. (1962). Method and means for recognizing complex patterns, US Patent 3069654.
  • Jing Wu, J. and Zhang, H. (2007). Camera sensor model for visual SLAM, IEEE Canadian Conference on Computer and Robot Vision (CRV), Montreal, Canada, pp. 149-156.
  • Jung, I. (2004). Simultaneous Localization and Mapping in 3D Environments with Stereovision, Ph.D. thesis, Institut National Polytechnique de Toulouse, Toulouse.
  • Kim, S. and Oh, S. (2008). SLAM in indoor environments using omni-directional vertical and horizontal line features, Journal of Intelligent Robotic Systems 51(1): 31-43.
  • Kitanov, A., Bisevac, S. and Petrovic, I. (2007). Mobile robot self-localization in complex indoor environments using monocular vision and 3D model, International Conference on Advanced Intelligent Mechatronics (ASME), Zurich, Switzerland, pp. 1-6.
  • Kwok, N.M., Dissanayake, G. and Ha., Q.P. (2005). Bearingonly SLAM using a SPRT based Gaussian sum filter, IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, pp. 1109-1114.
  • Lee, Y. and Song, J. (2007). Autonomous selection, registration, and recognition of objects for visual SLAM in indoor environments, International Conference on Control, Automation and Systems (ICCAS), Seoul, Korea, pp. 668-673.
  • Lemaire, T. and Lacroix, S. (2007). Monocular-vision based SLAM using line segments, IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy, pp. 2791-2796.
  • Li, C., Huang, Y., Kang, Y. and Yuan, J. (2008). Monocular SLAM using vertical straight lines with inverse-depth representation, World Congress on Intelligent Control and Automation (WCICA), Chongqing, China, pp. 3015-3020.
  • Mansinghka, V.K. (2004). Towards visual SLAM in dynamic environments, Report, MIT, Cambridge, MA.
  • Martinez-Carranza, J. and Calway, A. (2009). Efficiently increasing map density in visual SLAM using planar features with adaptive measurements, British Machine Vision Conference (BMVC), London, UK, pp. 1-11.
  • Marzorati, D., Matteucci, M., Migliori, D. and Sorrenti, D. (2009). On the use of inverse scaling in monocular SLAM, IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, pp. 2030-2036.
  • Moreno, F., Blanco, J. and Gonzalez, J. (2009). Stereo vision specific models for particle filter-based SLAM, Robotics and Autonomous Systems 57(9): 955-970.
  • Skrzypczyński, P. (2009). Simultaneous localization and mapping: A feature-based probabilistic approach, International Journal of Applied Mathematics and Computer Science 19(4): 575-588, DOI: 10.2478/v10006-009-0045-z.
  • Smith, P., Reid, I. and Davison, A. (2006). Real-time monocular SLAM with straight lines, British Machine Vision Conference (BMVC), Edinburgh, UK, pp. 17-27.
  • Thrun, S., Burgard, W. and Dieter, F. (2005). Probabilistic Robotics, MIT Press, Cambridge, MA.
  • Williams, B., Cummins, M., Neira, J., Newman, P., Reid, I. and Tardos, J. (2009). A comparison of loop closing techniques in monocular SLAM, Robotics and Autonomous Systems 57(12): 1188-1197.
  • Wongphati, M., Niparnan, N. and Sudsang, A. (2009). Bearing only FastSLAM using vertical line information from an omnidirectional camera, IEEE International Conference on Robotics and Biomimetics (ROBIO), Guilin, China, pp. 1188-1193.
  • Wu, E., Zhao, L., Guo, Y., Zhou, W. and Wang, Q. (2010). Monocular vision SLAM based on key feature points selection, IEEE International Conference on Robotics and Automation (ICRA), Povoa de Varzim, Portugal, pp. 1741-1745.
  • Wu, M., Huang, F., Wang, L. and Sun, J. (2009). Cooperative multi-robot monocular-SLAM using salient landmarks, International Asian Conference on Informatics in Control, Automation and Robotics (CAR), Bangkok, Thailand, pp. 151-155.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv22i2p409bwm
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