Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform

Ravi, Radhika; Lin, Yun-Jou; Elbahnasawy, Magdy; Shamseldin, Tamer; Habib, Ayman

doi:10.1109/jstars.2018.2812796

Cited by 75 publications

(56 citation statements)

References 29 publications

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“…The positional accuracy, on the other hand, is in the range of 0.01 to 0.02 m [15]. A rigorous system calibration procedure [16] is used for the estimation of the mounting parameters-spatial and rotational offsets-between the GNSS/INS and laser scanning units. The system calibration parameters are estimated through minimizing the discrepancies between conjugate points, linear features, and planar features captured from different drive-runs.…”

Section: Data Acquisition System Specifications and Configuration Of mentioning

confidence: 99%

A Mobile LiDAR for Monitoring Mechanically Stabilized Earth Walls with Textured Precast Concrete Panels

et al. 2020

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Mechanically Stabilized Earth (MSE) walls retain soil on steep, unstable slopes with crest loads. Over the last decade, they are becoming quite popular due to their high cost-to-benefit ratio, design flexibility, and ease of construction. Like any civil infrastructure, MSE walls need to be continuously monitored according to transportation asset management criteria during and after the construction stage to ensure that their expected serviceability measures are met and to detect design and/or construction issues, which could lead to structural failure. Current approaches for monitoring MSE walls are mostly qualitative (e.g., visual inspection or examination). Besides being time consuming, visual inspection might have inconsistencies due to human subjectivity. This research focuses on a comprehensive strategy using a mobile LiDAR mapping System (MLS) for the acquisition and processing of point clouds covering the MSE wall. The processing strategy delivers a set of global and local performance measure for MSE walls. Moreover, it is also capable of handling MSE walls with smooth or textured panels with the latter being the focus of this research due to its more challenging nature. For this study, an ultra-high-accuracy wheel-based MLS has been developed to efficiently acquire reliable data conducive to the development of the serviceability measures. To illustrate the feasibility of the proposed acquisition/processing strategy, two case studies in this research have been conducted with the first one focusing on the comparative performance of static and mobile LiDAR in terms of the agreement of the derived serviceability measures. The second case study aims at illustrating the feasibility of the proposed strategy in handling large textured MSE walls. Results from both case studies confirm the potential of using MLS for efficient, economic, and reliable monitoring of MSE walls.

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Section: Data Acquisition System Specifications and Configuration Of mentioning

confidence: 99%

A Mobile LiDAR for Monitoring Mechanically Stabilized Earth Walls with Textured Precast Concrete Panels

et al. 2020

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“…The USGS Simultaneous Multi-frame Analytical Calibration (SMAC) distortion model is adopted, which determines the principal distance c, principal point coordinates (x p , y p ), and radial and de-centering lens distortion coefficients (K 1 , K 2 , P 1 , P 2 ). The relative position and orientation parameters (hereafter, denoted as mounting parameters) between the onboard sensors (LiDAR and camera) and the GNSS/INS unit are determined through a rigorous system calibration [43,44]. The system calibration simultaneously estimates the mounting parameters by minimizing the discrepancies among conjugate points, linear features, and planar features obtained from LiDAR point clouds and images from different flight lines.…”

Section: Uav-based Mobile Mapping System Integration and System Calibmentioning

confidence: 99%

Evaluation of UAV LiDAR for Mapping Coastal Environments

et al. 2019

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Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively poor results in low textured areas as compared to those from LiDAR. This study demonstrates the applicability of UAV LiDAR for mapping coastal environments. A custom-built UAV-based mobile mapping system is used to simultaneously collect LiDAR and imagery data. The quality of LiDAR, as well as image-based point clouds, are investigated and compared over different geomorphic environments in terms of their point density, relative and absolute accuracy, and area coverage. The results suggest that both UAV LiDAR and image-based techniques provide high-resolution and high-quality topographic data, and the point clouds generated by both techniques are compatible within a 5 to 10 cm range. UAV LiDAR has a clear advantage in terms of large and uniform ground coverage over different geomorphic environments, higher point density, and ability to penetrate through vegetation to capture points below the canopy. Furthermore, UAV LiDAR-based data acquisitions are assessed for their applicability in monitoring shoreline changes over two actively eroding sandy beaches along southern Lake Michigan, Dune Acres, and Beverly Shores, through repeated field surveys. The results indicate a considerable volume loss and ridge point retreat over an extended period of one year (May 2018 to May 2019) as well as a short storm-induced period of one month (November 2018 to December 2018). The foredune ridge recession ranges from 0 m to 9 m. The average volume loss at Dune Acres is 18.2 cubic meters per meter and 12.2 cubic meters per meter within the one-year period and storm-induced period, respectively, highlighting the importance of episodic events in coastline changes. The average volume loss at Beverly Shores is 2.8 cubic meters per meter and 2.6 cubic meters per meter within the survey period and storm-induced period, respectively.

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“…As mentioned earlier, system calibration is a key step towards achieving accurately georeferenced products from GNSS/INS-assisted mobile mapping systems. In this research, the mounting parameters, defined by the boresight angles and lever arm components, between the GNSS/INS unit and other onboard sensors (i.e., LiDAR and RGB/hyperspectral cameras) are rigorously determined using the calibration strategy introduced in References [28,29]. In this strategy, the mounting parameters are simultaneously estimated through minimizing the discrepancies among linear/planar features and conjugate points extracted from LiDAR point clouds and images from different flight lines.…”

Section: Data Acquisition Systemmentioning

confidence: 99%

“…Such system calibration considers both spatial and temporal aspects. Spatial system calibration parameters include internal characteristics of the onboard camera(s), known as interior orientation parameters (IOPs), as well as mounting parameters which describe the differences in the position and orientation between the GNSS/INS body frame and camera(s) frame [28][29][30]. On the other hand, temporal system calibration aims at solving and correcting for any possible time delay in the synchronization between the GNSS/INS unit and the camera(s) onboard the UAV system [31,32].…”

Section: Introductionmentioning

confidence: 99%

GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

2020

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Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.

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Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform

Cited by 75 publications

References 29 publications

A Mobile LiDAR for Monitoring Mechanically Stabilized Earth Walls with Textured Precast Concrete Panels

A Mobile LiDAR for Monitoring Mechanically Stabilized Earth Walls with Textured Precast Concrete Panels

Evaluation of UAV LiDAR for Mapping Coastal Environments

GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

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