In traditional attitude mounting misalignment estimation methods for the calibration of unmanned autonomous vehicle (UAV) based light detection and ranging (LiDAR) system, signalized targets and iterative corresponding models are required, which makes it highly cost and computationally time-consuming. This paper presents an attitude mounting misalignment estimation (AMME) method for the calibration of UAV LiDAR system. The proposed method is divided into the coarse registration of LiDAR strips and the estimation of the attitude mounting misalignment. Firstly, 3D keypoints are extracted in the point clouds using the scale-invariant feature transform (SIFT) algorithm. Afterwards, the point feature transform (PFH) descriptor is used for 3D keypoint matching. Then, the coarse registration is executed. In the second part of the contribution, the systematic errors in the attitude mounting misalignment are estimated by incorporating the proposed triangular irregular network (TIN) corresponding model into the calibration modelling. Using the TIN-based corresponding model saves time and cost for AMME method. Furthermore, it provides two important effects: practical and computational, as no designed calibration boards, segmentation and iterative matching are needed. The performance of the proposed method is demonstrated under an UAV LiDAR data onboarded with lightweight navigation sensors. The experimental results show the efficacy of the method in comparison with a state-of-the-art method.
J. Kilian, N. Haala, M. Englich, “Capture and evaluation of airborne laser scanner data”, In Proceedings of International Archives of the Photogram., Remote Sens. and Spatial Inform. Sciences, Vienna, Austria, 12-18 July 1996, pp. 383-388.
D. Latypov, “Estimating relative LiDAR accuracy information from overlapping flight lines”, ISPRS J. Photogramm. Remote Sens., vol. 56, pp. 236-245, 2002.
G. Vosselman, “Strip offset estimation using linear features”, In Proceedings of the 3rd International Workshop on Mapping Geo-Surfical Processes Using Laser Altimetry, Columbus, OH, USA, 2002, pp. 1-9.
C. Van der Sande, S. Soudarissanane, K. Khoshelham, “Assessment of relative accuracy of AHN-2 laser scanning data using planar features”, Sensors, vol. 10, pp. 8198–8214, 2010.
S. Filin, “Recovery of systematic biases in laser altimetry data using natural surfaces”, Photogramm. Eng. and Remote Sens. Vol. 69, pp. 1235-1242, 2003.
J. Skaloud, D. Lichti, “Rigorous approach to bore-sight self-calibration in airborne laser scanning”, ISPRS J. Photogram. Remote Sens. Vol. 61, pp. 47–59, 2006.
A. Habib, A.P. Kersting, K.I. Bang, D.C. Lee, “Alternative Methodologies for the Internal Quality Control of Parallel LiDAR Strips”, IEEE Trans. Geosci. Remote Sens., vol. 48, pp. 221-236, 2010.
P. Besl, N.D. Mckay, “A method for registration of 3-D shapes”, IEEE Trans. Pattern Anal., Mach. Intell., vol. 14, pp. 239-256, 1992.
P. Glira, N. Pfeifer, G. Mandlburger, “Rigorous strip adjustment of UAV-based laserscanning data including time-dependent correction of trajectory errors”, Photogramm. Eng. Remote Sens. vol. 82, pp. 945-954, 2016.
R. Ravi, T. Shamseldin, M. Elbahnasawy, Y.J. Lin, A. Habib, “ Bias Impact Analysis and Calibration of UAV-Based Mobile LiDAR System with Spinning Multi-Beam Laser Scanner”, Applied Sciences. Vol. 8, pp. 297, 2018.
Z. Li, J. Tan, H. Liu, “Rigorous Boresight Self-Calibration of Mobile and UAV LiDAR Scanning Systems by Strip Adjustment”, Remote Sens., vol. 11, pp. 420-442, 2019.
X. Zhang, R. Gao, Q. Sun, J. Cheng, “An automated rectification method for unmanned aerial vehicle lidar point cloud data based on laser intensity”, Remote Sens., vol. 11, pp. 811–830, 2019.
E.M. de Oliveira Jr, D.R. dos Santos, “Rigorous Calibration of UAV-Based LiDAR Systems with Refinement of the Boresight Angles Using a Point-to-Plane Approach”, Sensors, vol. 19, p. 5224, 2019.
R.B. Rusu, N. Blodow, Z. Marton, A. Soos, M. Beetz, “Towards 3D object maps for autonomous household robots” In International Conference on Intelligent Robots and Systems, IEEE. https://doi.org/10.1109/iros.2007.4399309, 2007.
R.B. Rusu, N. Blodow, M. Beetz, “Fast point feature histograms (FPFH) for 3D registration”, IEEE International Conference on obotics and Automation (ICRA), pp. 3212-3217, 2009.
K. Zhang, S. Chen, D. Whitman, M. Shyu, J., Yan, C. Zhang, “A progressive morphological filter for removing nonground measurements from airborne LiDAR data” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, pp. 872-882, 2003.
M.A. Fischler, R.C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography”. Comm. of the ACM, vol. 24, pp. 42-65, 1981.
E.M. Mikhail, J.S. Bethel, J.C McGlone, “Introduction to Modern Photogrammetry”, New York. John Wiley & Sons, Inc. 2001. 479 p.
B.K. Horn “Closed-form solution of absolute orientation using unit quaternions,” J. Opt. Soc. Am., vol. 4, pp. 629-642, 1987.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2020 Array