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Monte Carlo Localization

Monte Carlo Localization (MCL) in Outdoor Environment
  • Researchers
    • Tae-Bum Kwon (Ph.D. )
    • Yong-Hoon Ji (M.S.)
    • Dong-Il Kim (Ph.D. Candidate, astronovic@korea.ac.kr)
  • Period of research
    • 2009. 7. ~ 2012. 12.
  • Research Background
    • Reference map : DEM/DSM (built by an aerial mapping system)
    • An elevation map cannot represent outdoor environments in detail.
    • 3-D environments are observed differently from the air and the ground.
    • Poor localization performance occurs when an elevation map is used as a reference map for outdoor environments.

    
          Fig. 1 Examples of discrepancy between elevation map and real range sensor data

    • COAG features : objects commonly observed from air and ground.
    • COAG features can be accurately mapped into the elevation map built by an aerial mapping system and also sensed by a range sensor mounted on a mobile robot.
                      
                                           Fig. 2 Examples of COAG features

    • Candidate Selection with Shape of Sensor Data
    • Hausdorff distance (H(A, B))and average of minimum distances are used for classifying the candidates of robot.

                                          
                                    Fig. 3 Example of Hausdorff distance and minimum distances

  • Research Objectives
    • Global localization and local tracking in outdoor environments
    • Monte Carlo Localization(MCL) : determination of the robot pose based on Markov localization in a given map

  • Research Output

                     Fig. 3 Experimental results of global localization

  • Demo Movie

  • Paper 1 :  Tae-Bum Kwon, Jae-Bok Song, A New Feature Commonly Observed from Air and Ground for Outdoor Localization with Elevation Map Built by Aerial Mapping System, Journal of Field Robotics, Vol. 28, No. 2, pp. 227-240, 2011.03. 
  • Paper 2 :  Yong-Hoon Ji, Jae-Bok Song, Joo-Hyun Baek, and Jae-Kwan Ryu, Hausdorff Distance Matching for Elevation Map-based Global Localization of an Outdoor Mobile Robot, Journal of Institute of Control, Robotics and Systems, Vol. 17, No. 9, pp. 1-6, 2011.
  • Paper 3 :  Dong-Il Kim, Jae-Bok Song, Ji-Hoon Choi, Improvement of Localization Accuracy with COAG Features and Candidate Selection based on Shape of Sensor Data, Journal of Korea Robotics Society, Vol. 9, No. 2, pp. 117-123, 2014.


* Last updated: 2014. 7. 01