Forestry crane posture estimation with a two-dimensional laser scanner

Heikki Hyyti (Corresponding Author), V.V. Lehtola, Arto Visala

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
20 Downloads (Pure)

Abstract

Crane posture estimation is the stepping stone to forest machine automation. Here, we introduce a robust minimal perception solution, that is, one that uses minimal constraints for maximal benefits. Specifically, we introduce a robust particle‐filter‐based method to estimate and track the posture of a flexible hydraulic crane by using only low‐cost equipment, namely, a two‐dimensional (2D) laser scanner, two short magnetically attached metal tubes as targets, and an angle sensor. An important feature of our method is that it incorporates control signals for hydraulic actuators. In contrast to the previous works employing laser scanners, we do not use the full shape of the crane to estimate the crane posture, but, instead, we use only two small targets in the field of view of the laser scanner. Thus, a large share of the range data is useful for other purposes, for example, to map the surrounding environment. We test the proposed method in a challenging forest environment and show that the particle filter is able to estimate the posture of the hydraulic crane efficiently and reliably in the presence of occlusions and obstructions. During our comprehensive testing, the tip position was measured with average errors smaller than 4.3 cm whereas the absolute maximum error was 15 cm.
Original languageEnglish
Pages (from-to)1025-1049
Number of pages25
JournalJournal of Field Robotics
Volume35
Issue number7
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Keywords

  • forestry
  • instrumentation
  • laser scanning
  • perception
  • position estimation
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • UT-Hybrid-D

Cite this

Hyyti, Heikki ; Lehtola, V.V. ; Visala, Arto. / Forestry crane posture estimation with a two-dimensional laser scanner. In: Journal of Field Robotics. 2018 ; Vol. 35, No. 7. pp. 1025-1049.
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abstract = "Crane posture estimation is the stepping stone to forest machine automation. Here, we introduce a robust minimal perception solution, that is, one that uses minimal constraints for maximal benefits. Specifically, we introduce a robust particle‐filter‐based method to estimate and track the posture of a flexible hydraulic crane by using only low‐cost equipment, namely, a two‐dimensional (2D) laser scanner, two short magnetically attached metal tubes as targets, and an angle sensor. An important feature of our method is that it incorporates control signals for hydraulic actuators. In contrast to the previous works employing laser scanners, we do not use the full shape of the crane to estimate the crane posture, but, instead, we use only two small targets in the field of view of the laser scanner. Thus, a large share of the range data is useful for other purposes, for example, to map the surrounding environment. We test the proposed method in a challenging forest environment and show that the particle filter is able to estimate the posture of the hydraulic crane efficiently and reliably in the presence of occlusions and obstructions. During our comprehensive testing, the tip position was measured with average errors smaller than 4.3 cm whereas the absolute maximum error was 15 cm.",
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Forestry crane posture estimation with a two-dimensional laser scanner. / Hyyti, Heikki (Corresponding Author); Lehtola, V.V.; Visala, Arto.

In: Journal of Field Robotics, Vol. 35, No. 7, 10.2018, p. 1025-1049.

Research output: Contribution to journalArticleAcademicpeer-review

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