Abstract
Laser scanning is a promising geometric data collection tool for construction, facility, and infrastructure management due to its fast sampling rate (tens of thousands of measurements per second) and millimeter-level accuracy. However, laser scanned data contains inaccurate data points at spatial discontinuities (object edges). These inaccurate points, known as mixed-pixels, are commonly removed from the data prior to geometric modeling or other downstream processes. The removal of points at the edges of objects introduces error in the geometry of the objects, and object dimensions extracted from the data, such as widths and heights, are usually smaller than the actual values. In many cases, these losses due to removal of points at edges can exceed measurement accuracy tolerances specified in inspection manuals. This paper proposes a model for estimating edge loss in laser scanned data by considering the impacts of various factors, such as scanning distance, density of data and incidence angle on the edge loss. Results from a series of controlled experiments showed that the developed model successfully predicted edge losses in most test cases. Evaluation results using data collected from job sites showed that this model reduced the measurement error due to edge loss by an average of 80% for dense point clouds collected by an amplitude modulated continuous wave (AMCW) scanner, and 38% for relatively sparse point clouds collected by a pulsed time of flight (PTOF) scanner. By adding the estimated edge losses back into the raw dimensional measurements using the developed model, it is possible to significantly improve the accuracy of related measurements and hence improve the accuracy of the geometric information extracted from laser scanned data.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1070-1083 |
| Number of pages | 14 |
| Journal | Automation in Construction |
| Volume | 18 |
| Issue number | 8 |
| DOIs | |
| State | Published - Dec 2009 |
| Externally published | Yes |
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Keywords
- Accuracy analysis
- Inspection
- Laser scanning
- Mixed-pixel
- Quality control
- Spatial discontinuity
ASJC Scopus subject areas
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction
Cite this
Quantification of edge loss of laser scanned data at spatial discontinuities. / Tang, Pingbo; Akinci, Burcu; Huber, Daniel.
In: Automation in Construction, Vol. 18, No. 8, 12.2009, p. 1070-1083.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Quantification of edge loss of laser scanned data at spatial discontinuities
AU - Tang, Pingbo
AU - Akinci, Burcu
AU - Huber, Daniel
PY - 2009/12
Y1 - 2009/12
N2 - Laser scanning is a promising geometric data collection tool for construction, facility, and infrastructure management due to its fast sampling rate (tens of thousands of measurements per second) and millimeter-level accuracy. However, laser scanned data contains inaccurate data points at spatial discontinuities (object edges). These inaccurate points, known as mixed-pixels, are commonly removed from the data prior to geometric modeling or other downstream processes. The removal of points at the edges of objects introduces error in the geometry of the objects, and object dimensions extracted from the data, such as widths and heights, are usually smaller than the actual values. In many cases, these losses due to removal of points at edges can exceed measurement accuracy tolerances specified in inspection manuals. This paper proposes a model for estimating edge loss in laser scanned data by considering the impacts of various factors, such as scanning distance, density of data and incidence angle on the edge loss. Results from a series of controlled experiments showed that the developed model successfully predicted edge losses in most test cases. Evaluation results using data collected from job sites showed that this model reduced the measurement error due to edge loss by an average of 80% for dense point clouds collected by an amplitude modulated continuous wave (AMCW) scanner, and 38% for relatively sparse point clouds collected by a pulsed time of flight (PTOF) scanner. By adding the estimated edge losses back into the raw dimensional measurements using the developed model, it is possible to significantly improve the accuracy of related measurements and hence improve the accuracy of the geometric information extracted from laser scanned data.
AB - Laser scanning is a promising geometric data collection tool for construction, facility, and infrastructure management due to its fast sampling rate (tens of thousands of measurements per second) and millimeter-level accuracy. However, laser scanned data contains inaccurate data points at spatial discontinuities (object edges). These inaccurate points, known as mixed-pixels, are commonly removed from the data prior to geometric modeling or other downstream processes. The removal of points at the edges of objects introduces error in the geometry of the objects, and object dimensions extracted from the data, such as widths and heights, are usually smaller than the actual values. In many cases, these losses due to removal of points at edges can exceed measurement accuracy tolerances specified in inspection manuals. This paper proposes a model for estimating edge loss in laser scanned data by considering the impacts of various factors, such as scanning distance, density of data and incidence angle on the edge loss. Results from a series of controlled experiments showed that the developed model successfully predicted edge losses in most test cases. Evaluation results using data collected from job sites showed that this model reduced the measurement error due to edge loss by an average of 80% for dense point clouds collected by an amplitude modulated continuous wave (AMCW) scanner, and 38% for relatively sparse point clouds collected by a pulsed time of flight (PTOF) scanner. By adding the estimated edge losses back into the raw dimensional measurements using the developed model, it is possible to significantly improve the accuracy of related measurements and hence improve the accuracy of the geometric information extracted from laser scanned data.
KW - Accuracy analysis
KW - Inspection
KW - Laser scanning
KW - Mixed-pixel
KW - Quality control
KW - Spatial discontinuity
UR - http://www.scopus.com/inward/record.url?scp=70349433529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349433529&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2009.07.001
DO - 10.1016/j.autcon.2009.07.001
M3 - Article
AN - SCOPUS:70349433529
VL - 18
SP - 1070
EP - 1083
JO - Automation in Construction
JF - Automation in Construction
SN - 0926-5805
IS - 8
ER -