Mobile robot navigation using grid line patterns via probabilistic measurement modeling

详细信息    查看全文

• 作者：Taeyun Kim ; Jinwhan Kim ; Hyun-Taek Choi
• 关键词：Indoor navigation ; Grid line patterns ; Particle filter ; Mobile robot
• 刊名：Intelligent Service Robotics
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：9
• 期：2
• 页码：141-151
• 全文大小：2,347 KB
• 参考文献：1.Blanc G, Mezouar Y,Martinet P (2005) Indoor navigation of a wheeled mobile robot along visual routes. In: Proceedings of the 2005 IEEE international conference on robotics and automation, 2005. ICRA 2005. IEEE, pp 3354–3359
  2.Hayet J-B, Lerasle F, Devy M (2007) A visual landmark framework for mobile robot navigation. Image Vis Comput 25(8):1341–1351CrossRef
  3.Matsumoto Y, Ikeda K, Inaba M, Inoue H (1999) Visual navigation using omnidirectional view sequence. In: Proceedings of 1999 IEEE/RSJ international conference on intelligent robots and systems, 1999. IROS’99, vol 1. IEEE, pp 317–322
  4.Matsumoto Y, Sakai K,Inaba M, Inoue H (2000) View-based approach to robot navigation. In: Proceedings of 2000 IEEE/RSJ international conference on intelligent robots and systems, 2000 (IROS 2000), vol 3. IEEE, pp 1702–1708
  5.Pears N, Liang B (2001) Ground plane segmentation for mobile robot visual navigation. In: Proceedings of 2001 IEEE/RSJ international conference on intelligent robots and systems, 2001, vol 3. IEEE, pp 1513–1518
  6.Dao NX, You B-J,Oh S-R (2005) Visual navigation for indoor mobile robots using a single camera. In: 2005 IEEE/RSJ international conference on intelligent robots and systems, 2005 (IROS 2005). IEEE, pp 1992–1997
  7.DeSouza GN, Kak AC (2002) Vision for mobile robot navigation: a survey. IEEE Trans Pattern Anal Mach Intell 24(2):237–267CrossRef
  8.Kak AC, DeSouza GN (2002) Robotic vision: what happened to the visions of yesterday? In: Proceedings of 16th international conference on pattern recognition, 2002, vol 2. IEEE, pp 839–847
  9.Abascal J, Lazkano E, Sierra B (2005) Behavior-based indoor navigation. In: Ambient intelligence for scientific discovery. Springer, Berlin, pp 263–285
  10.Bonin-Font F, Ortiz A, Oliver G (2008) Visual navigation for mobile robots: a survey. J Intell Robot Syst 53(3):263–296CrossRef
  11.Lee Y-J, Yim B-D, Song J-B (2009) Mobile robot localization based on effective combination of vision and range sensors. Int J Control Autom Syst 7(1):97–104CrossRef
  12.Correa DSO, Sciotti DF, Prado MG, Sales DO, Wolf DF, Osorio FS (2012) Mobile robots navigation in indoor environments using kinect sensor. In: 2012 Second Brazilian conference on critical embedded systems (CBSEC). IEEE, pp 36–41
  13.Biswas J, Veloso M (2012) Depth camera based indoor mobile robot localization and navigation. In: 2012 IEEE International conference on robotics and automation (ICRA). IEEE, pp 1697–1702
  14.Buschmann C, Muller F, Stefan F (2004) Grid-based navigation for autonomous, mobile robots. In: Proceedings of 1st workshop on positioning, navigation and communication 2004 (WPNC’04). Shaker Publishing
  15.Yean YP, Chetty RMK (2012) An efficient grid based navigation of wheeled mobile robots based on visual perception. In: Trends in intelligent robotics, automation, and manufacturing. Springer, Berlin, pp 128–135
  16.Yan J (2012) Improving mobile robot localization: grid-based approach. Opt Eng 51(2):024401-1–024401-7CrossRef
  17.Mächler P (1997) Robot odometry correction using grid lines on the floor. In: Proceedings of 2nd international workshop on mechatronical computer systems for perception and action, Pisa, Italy
  18.Kim T, Kim J (2013) Beaconless navigation for mobile robots using grid line pattern. In: 2013 10th international conference on ubiquitous robots and ambient intelligence (URAI). IEEE, pp 200–205
  19.Matas J, Galambos C, Kittler J (2000) Robust detection of lines using the progressive probabilistic hough transform. Comput Vis Image Underst 78(1):119–137CrossRef
  
• 作者单位：Taeyun Kim (1)
  Jinwhan Kim (1)
  Hyun-Taek Choi (2)
  
  1. Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-338, Korea
  2. Korea Research Institute of Ships and Ocean Engineering, 32, Yuseong-daero 1312 beon-gil, Yuseong-gu, Daejeon, 305-343, Korea
  
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Control Engineering
  Artificial Intelligence and Robotics
  User Interfaces and Human Computer Interaction
  Vibration, Dynamical Systems and Control
  Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1861-2784

文摘

Mobile robots are generally equipped with proprioceptive motion sensors such as odometers and inertial sensors. These sensors are used for dead-reckoning navigation in an indoor environment where GPS is not available. However, this dead-reckoning scheme is susceptible to drift error in position and heading. This study proposes using grid line patterns which are often found on the surface of floors or ceilings in an indoor environment to obtain pose (i.e., position and orientation) fix information without additional external position information by artificial beacons or landmarks. The grid lines can provide relative pose information of a robot with respect to the grid structure and thus can be used to correct the pose estimation errors. However, grid line patterns are repetitive in nature, which leads to difficulties in estimating its configuration and structure using conventional Gaussian filtering that represent the system uncertainty using a unimodal function (e.g., Kalman filter). In this study, a probabilistic sensor model to deal with multiple hypotheses is employed and an online navigation filter is designed in the framework of particle filtering. To demonstrate the performance of the proposed approach, an experiment was performed in an indoor environment using a wheeled mobile robot, and the results are presented.