Vehicle positioning system based on passive planar image markers

In this paper we present an approach for optical vehicle positioning (e.g. fork lifters). Our approach is motivated by planar marker detection systems like ARTag or ARToolkit, in which poses of planar markers relative to the camera can be determined. In contrast to existing optical positioning systems(e.g. SkyTrax), we mount cameras on the ceiling and passive (non-electronic) planar markers on the top of the vehicles. The absence of complex electronic components on the high stressed vehicles enables rapid process integration, which is particularly important for rental vehicles. We have evaluated our method keeping the most important user requirements coverage, costs and accuracy under consideration for three intra-logistic scenarios a) zone monitoring with zone precise positioning, b) storage aisle monitoring with storage place precise positioning and, c) complete driving range monitoring with maximum precision.

[1]  Fan Xiao,et al.  What is the best fiducial? , 2002, The First IEEE International Workshop Agumented Reality Toolkit,.

[2]  Chao Huang,et al.  A Survey on Indoor Positioning Technologies , 2011 .

[3]  Klaus Richter,et al.  Virtual Top View: Towards Real-Time Agregation of Videos to Monitor Large Areas , 2013, CAIP.

[4]  Mark Fiala Vision guided control of multiple robots , 2004, First Canadian Conference on Computer and Robot Vision, 2004. Proceedings..

[5]  R. Mautz Indoor Positioning Technologies , 2012 .

[6]  Axel Pinz,et al.  Robust Pose Estimation from a Planar Target , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[9]  Klaus Richter,et al.  A Case Study of Radio-Based Monitoring System for Enhanced Safety of Logistics Processes , 2012, SAFECOMP Workshops.

[10]  Gary R. Bradski,et al.  Learning OpenCV - computer vision with the OpenCV library: software that sees , 2008 .

[11]  Thomas M. Breuel,et al.  Efficient implementation of local adaptive thresholding techniques using integral images , 2008, Electronic Imaging.

[12]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[13]  Sebastian Tilch,et al.  Survey of optical indoor positioning systems , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.