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Title: Bias Impact Analysis and Calibration of UAV-Based Mobile LiDAR System with Spinning Multi-Beam Laser Scanner

Light Detection and Ranging (LiDAR) is a technology that uses laser beams to measure ranges and generates precise 3D information about the scanned area. It is rapidly gaining popularity due to its contribution to a variety of applications such as Digital Building Model (DBM) generation, telecommunications, infrastructure monitoring, transportation corridor asset management and crash/accident scene reconstruction. To derive point clouds with high positional accuracy, estimation of mounting parameters relating the laser scanners to the onboard Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) unit, i.e., the lever-arm and boresight angles, is the foremost and necessary step. This paper proposes a LiDAR system calibration strategy for a Unmanned Aerial Vehicle (UAV)-based mobile mapping system that can directly estimate the mounting parameters for spinning multi-beam laser scanners through an outdoor calibration procedure. This approach is based on the use of conjugate planar/linear features in overlapping point clouds derived from different flight lines. Designing an optimal configuration for calibration is the first and foremost step in order to ensure the most accurate estimates of mounting parameters. This is achieved by conducting a rigorous theoretical analysis of the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud. Themore » dependency of the impact on the orientation of target primitives and relative flight line configuration would help in deducing the configuration that would maximize as well as decouple the impact of bias in each mounting parameter so as to ensure their accurate estimation. Finally, the proposed analysis and calibration strategy are validated by calibrating a UAV-based LiDAR system using two different datasets—one acquired with flight lines at a single flying height and the other with flight lines at two different flying heights. With this being said, the calibration performance is evaluated by analyzing correlation between the estimated system parameters, the a-posteriori variance factor of the Least Squares Adjustment (LSA) procedure and the quality of fit of the adjusted point cloud to planar/linear features before and after the calibration process.« less
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Purdue Univ., West Lafayette, IN (United States)
Publication Date:
Grant/Contract Number:
AR0000593
Type:
Accepted Manuscript
Journal Name:
Applied Sciences
Additional Journal Information:
Journal Volume: 8; Journal Issue: 2; Journal ID: ISSN 2076-3417
Publisher:
MDPI
Research Org:
Purdue Univ., West Lafayette, IN (United States)
Sponsoring Org:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; LiDAR; UAV; laser scanners; 3D point clouds; GNSS/INS; calibration; bias impact analysis; Least Squares Adjustment
OSTI Identifier:
1501838

Ravi, Radhika, Shamseldin, Tamer, Elbahnasawy, Magdy, Lin, Yun-Jou, and Habib, Ayman. Bias Impact Analysis and Calibration of UAV-Based Mobile LiDAR System with Spinning Multi-Beam Laser Scanner. United States: N. p., Web. doi:10.3390/app8020297.
Ravi, Radhika, Shamseldin, Tamer, Elbahnasawy, Magdy, Lin, Yun-Jou, & Habib, Ayman. Bias Impact Analysis and Calibration of UAV-Based Mobile LiDAR System with Spinning Multi-Beam Laser Scanner. United States. doi:10.3390/app8020297.
Ravi, Radhika, Shamseldin, Tamer, Elbahnasawy, Magdy, Lin, Yun-Jou, and Habib, Ayman. 2018. "Bias Impact Analysis and Calibration of UAV-Based Mobile LiDAR System with Spinning Multi-Beam Laser Scanner". United States. doi:10.3390/app8020297. https://www.osti.gov/servlets/purl/1501838.
@article{osti_1501838,
title = {Bias Impact Analysis and Calibration of UAV-Based Mobile LiDAR System with Spinning Multi-Beam Laser Scanner},
author = {Ravi, Radhika and Shamseldin, Tamer and Elbahnasawy, Magdy and Lin, Yun-Jou and Habib, Ayman},
abstractNote = {Light Detection and Ranging (LiDAR) is a technology that uses laser beams to measure ranges and generates precise 3D information about the scanned area. It is rapidly gaining popularity due to its contribution to a variety of applications such as Digital Building Model (DBM) generation, telecommunications, infrastructure monitoring, transportation corridor asset management and crash/accident scene reconstruction. To derive point clouds with high positional accuracy, estimation of mounting parameters relating the laser scanners to the onboard Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) unit, i.e., the lever-arm and boresight angles, is the foremost and necessary step. This paper proposes a LiDAR system calibration strategy for a Unmanned Aerial Vehicle (UAV)-based mobile mapping system that can directly estimate the mounting parameters for spinning multi-beam laser scanners through an outdoor calibration procedure. This approach is based on the use of conjugate planar/linear features in overlapping point clouds derived from different flight lines. Designing an optimal configuration for calibration is the first and foremost step in order to ensure the most accurate estimates of mounting parameters. This is achieved by conducting a rigorous theoretical analysis of the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud. The dependency of the impact on the orientation of target primitives and relative flight line configuration would help in deducing the configuration that would maximize as well as decouple the impact of bias in each mounting parameter so as to ensure their accurate estimation. Finally, the proposed analysis and calibration strategy are validated by calibrating a UAV-based LiDAR system using two different datasets—one acquired with flight lines at a single flying height and the other with flight lines at two different flying heights. With this being said, the calibration performance is evaluated by analyzing correlation between the estimated system parameters, the a-posteriori variance factor of the Least Squares Adjustment (LSA) procedure and the quality of fit of the adjusted point cloud to planar/linear features before and after the calibration process.},
doi = {10.3390/app8020297},
journal = {Applied Sciences},
number = 2,
volume = 8,
place = {United States},
year = {2018},
month = {2}
}