Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W4, 55-60, 2016
https://doi.org/10.5194/isprs-archives-XL-3-W4-55-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
17 Mar 2016
CALIBRATION AND STABILITY ANALYSIS OF THE VLP-16 LASER SCANNER
C. L. Glennie1, A. Kusari1, and A. Facchin2 1Dept. of Civil & Environmental Engineering, University of Houston, Houston, TX , USA
2Geosoft S.R.L., Viale Lino Zanussi 8D, 33170 Pordenone, IT
Keywords: Velodyne, Stability, Calibration, LiDAR Abstract. We report on a calibration and stability analysis of the Velodyne VLP-16 LiDAR scanner. The sensor is evaluated for long-term stability, geometric calibration and the effect of temperature variations. To generalize the results, three separate VLP-16 sensors were examined. The results and conclusions from the analysis of each of the individual sensors was similar. We found that the VLP-16 showed a consistent level of performance, in terms of range bias and noise level over the tested temperature range from 0–40 °C. A geometric calibration was able to marginally improve the accuracy of the VLP-16 point cloud (by approximately 20%) for a single collection, however the temporal stability of the geometric calibration negated this accuracy improvement. Overall, it was found that there is some long-term walk in the ranging observations from individual lasers within the VLP-16, which likely causes the instability in the determination of geometric calibration parameters. However, despite this range walk, the point cloud delivered from the VLP-16 sensors tested showed an accuracy level within the manufacturer specifications of 3 cm RMSE, with an overall estimated RMSE of range residuals between 22 mm and 27 mm.
Conference paper (PDF, 1195 KB)


Citation: Glennie, C. L., Kusari, A., and Facchin, A.: CALIBRATION AND STABILITY ANALYSIS OF THE VLP-16 LASER SCANNER, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W4, 55-60, https://doi.org/10.5194/isprs-archives-XL-3-W4-55-2016, 2016.

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