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  • 1.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hohnloser, Peter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tree diameter estimation using laser scanner2012Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Accurate vehicle localization in forest environments is still an unresolved problem. GPS has obvious limitations in dense forest, and has to be mixed with other techniques to provide satisfying solutions. One possible way is to localize the vehicle relative to trees detected around the vehicle. The first step to implement this method is is to find reliable methods to detect trees, and also to match them to maps. The reliability of this matching operation is improved by accurate estimations of tree diameter. In this paper we evaluate a number of existing algorithms for detection of trees and estimation of tree diameter. Three new algorithms are also suggested. All algorithms were evaluated in field experiments at three different locations with varying tree trunk visibility. The results show that one of the existing algorithms is clearly less reliable than the other two. Noticeable is that the existing algorithms often overestimate tree trunk diameter. The new algorithms mostly underestimate, but are most accurate in some situations. 

  • 2.
    Hellström, Thomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Lindahl, Olof
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Bäcklund, Tomas
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Karlsson, Marcus
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Hohnloser, Peter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden.
    Bråndal, Anna
    Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. University Hospital of Northern Sweden, Umeå, Sweden.
    Hu, Xiaolei
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering. University Hospital of Northern Sweden, Umeå, Sweden; Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Sweden.
    Wester, Per
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. University Hospital of Northern Sweden, Umeå, Sweden.
    An intelligent rollator for mobility impaired persons, especially stroke patients2016Ingår i: Journal of Medical Engineering & Technology, ISSN 0309-1902, E-ISSN 1464-522X, Vol. 40, nr 5, s. 270-279Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An intelligent rollator (IRO) was developed that aims at obstacle detection and guidance to avoid collisions and accidental falls. The IRO is a retrofit four-wheeled rollator with an embedded computer, two solenoid brakes, rotation sensors on the wheels and IR-distance sensors. The value reported by each distance sensor was compared in the computer to a nominal distance. Deviations indicated a present obstacle and caused activation of one of the brakes in order to influence the direction of motion to avoid the obstacle. The IRO was tested by seven healthy subjects with simulated restricted and blurred sight and five stroke subjects on a standardised indoor track with obstacles. All tested subjects walked faster with intelligence deactivated. Three out of five stroke patients experienced more detected obstacles with intelligence activated. This suggests enhanced safety during walking with IRO. Further studies are required to explore the full value of the IRO.

  • 3.
    Hohnloser, Peter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Tree identificationand trunk diameter estimation with a 2D laser scanner2013Ingår i: Proceedings of Umeå's 16th student conference in computing science: USCCS 2013 / [ed] Suna Bensch & Frank Drewes, Umeå: Umeå universitet , 2013, s. 27-38Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    This paper presents an algorithm for identifying trees in a 2Dlaser scan and six different tree trunk diameter estimation methods. Thetree identification algorithm turned out to be very sensitive and hence notalways as reliable as one could wish. Of the tree trunk estimation methodsthree were developed during this work. All methods were tested andcompared in an experimental way to find out which is most appropriateto use in forestry. The experiment was conducted on three positionswhere the tree trunks visibility differed. The result shows that one ofthe existing methods is not as reliable as the other ones and the othertwo existing ones give a similar result. Noticeable is that the existingmethods overestimates the tree trunk diameter and the newly developedmethods mostly underestimates it, but as the experiment shows givesthe most accurate result in some positions.

  • 4.
    Lindroos, Ola
    et al.
    SLU.
    Ringdahl, Ola
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Pedro, La Hera
    SLU.
    Hohnloser, Peter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Estimating the position of the harvester head: a key step towards the precision forestry of the future?2015Ingår i: Croatian Journal of Forest Engineering, ISSN 1845-5719, E-ISSN 1848-9672, Vol. 36, nr 2, s. 147-164Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Modern harvesters are technologically sophisticated, with many useful features such as the ability to automatically measure stem diameters and lengths. This information is processed in real time to support value optimization when cutting stems into logs. It can also be transferred from the harvesters to centralized systems and used for wood supply management. Such information management systems have been available since the 1990s in Sweden and Finland, and are constantly being upgraded. However, data on the position of the harvester head relative to the machine are generally not recorded during harvesting. The routine acquisition and analysis of such data could offer several opportunities to improve forestry operations and related processes in the future. Here, we analyze the possible benefits of having this information, as well as the steps required to collect and process it. The benefits and drawbacks of different sensing technologies are discussed in terms of potential applications, accuracy and cost. We also present the results of preliminary testing using two of the proposed methods. Our analysis indicates that an improved scope for mapping and controlling machine movement is the main benefit that is directly related to the conduct of forestry operations. In addition, there are important indirect benefits relating to ecological mapping. Our analysis suggests that both of these benefits can be realized by measuring the angles of crane joints or the locations of crane segments and using the resulting information to compute the head's position. In keeping with our findings, two companies have recently introduced sensor equipped crane solutions.

  • 5.
    Ringdahl, Ola
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hohnloser, Peter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Hellström, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Holmgren, Johan
    Dept. of Forest Resource Management, Swedish University of Agricultural Sciences.
    Lindroos, Ola
    Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences.
    Enhanced Algorithms for Estimating Tree Trunk Diameter Using 2D Laser Scanner2013Ingår i: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 5, nr 10, s. 4839-4856Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Accurate vehicle localization in forest environments is still an unresolved problem. Global navigation satellite systems (GNSS) have well known limitations in dense forest, and have to be combined with for instance laser based SLAM algorithms to provide satisfying accuracy. Such algorithms typically require accurate detection of trees, and estimation of tree center locations in laser data. Both these operations depend on accurate estimations of tree trunk diameter. Diameter estimations are important also for several other forestry automation and remote sensing applications. This paper evaluates several existing algorithms for diameter estimation using 2D laser scanner data. Enhanced algorithms, compensating for beam width and using multiple scans, were also developed and evaluated. The best existing algorithms overestimated tree trunk diameter by ca. 40%. Our enhanced algorithms, compensating for laser beam width, reduced this error to less than 12%.

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