Summary
For the last decades, measurement and automation systems in Nordic cut-to-length forestry machines have evolved gradually. These heavy duty machines are lighter, faster and more accurate than ever before but the basic technologies and operation have remained the same. In many respects, their current automation systems have reached their limits. The Forestrix project studies how advances in mobile robotics could be applied in the field of forestry machine automation. Machine vision systems and scanning laser range finders have established themselves as standard equipment in mobile robotics. With the new sensor and computing technologies it is possible to get information about the surrounding forest, such as tree diameters, positions and stand density. This information can be used on-line in operator’s decision support system, or off-line in a forest asset management system. This paper describes the prototype measurement platform and the software algorithms developed in the Forestrix project. Results from tests with an all terrain vehicle are also presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hellström, T., Johansson, T., Ringdahl, O., Georgsson, F., Prorok, K., Sandström, U.: Development of an Autonomous Path Tracking Forest Machine. In: The 5th International Conference on Field and Service Robotics (FSR 2005), Port Douglas, Australia, July 29–31 (2005)
Miettinen, M., Öhman, M., Visala, A., Forsman, P.: Simultaneous Localization and Mapping for Forest Harvesters. In: The 2007 IEEE International Conference on Robotics and Automation (ICRA 2007), Rome, Italy, April 10–14 (2007)
Ziou, D., Tabbone, S.: Edge detection techniques – an overview. International Journal of Pattern Recognition and Image Analysis 8, 537–559 (1998)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Kovesi, P.: Image features from phase congruency. Videre: Journal of Computer Vision Research 1(3) (1999)
Kovesi, P.: Edges are not just steps. In: The 5th Asian Conference on Computer Vision (ACCV 2002) (January 2002)
Kovesi, P.: MATLAB and Octave functions for computer vision and image processing. School of Computer Science & Software Engineering, The University of Western Australia (Referenced 4.12.2006) (2006), http://www.csse.uwa.edu.au/~pk/research/matlabfns/
Fleck, M.: Some defects in finite-difference edge finders. IEEE Trans. Pattern Analysis and Machine Intelligence 14(3) (March 1992)
Mallon, J., Whelan, P.: Precise radial un-distortion of images. In: Proc. the 17th Int. Conf. on Pattern Recognition (ICPR 2004) (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Öhman, M., Miettinen, M., Kannas, K., Jutila, J., Visala, A., Forsman, P. (2008). Tree Measurement and Simultaneous Localization and Mapping System for Forest Harvesters. In: Laugier, C., Siegwart, R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75404-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-540-75404-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75403-9
Online ISBN: 978-3-540-75404-6
eBook Packages: EngineeringEngineering (R0)