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We assess the accuracy of Structure-from-Motion/Multiview stereo (SM) terrain models acquired ad hoc or without high-resolution ground control to analyze their usage as a base for inexpensive 3D bedrock geologic mapping. Our focus is on techniques that can be utilized in field projects without the use of heavy and/or expensive equipment or the placement of ground control in logistically challenging sites (e.g., steep cliff faces or remote settings). We use a Terrestrial Light Detection and Ranging (LiDAR) survey as a basis for the comparison of two types of SM models: (1) models developed from images acquired in a chartered airplane flight with ground control referenced by natural objects located on Google Earth scenes; and (2) drone flights with a georeference established solely from camera positions located by conventional, differentially corrected Global Navigation Satellite systems (GNSS). We find that all our SM models are indistinguishable in scale from the LiDAR reference model. The SM models do, however, show rigid body translations and rotations, with translations generally within the 1–5 m size of the natural objects used for ground control, the resolution of the GNSS receivers, or both. The rigid body rotations can be attributed to a poor imaging plan, which can be avoided with survey planning. Analyses of point densities in various models show a limitation of Terrestrial LiDAR point clouds as a mapping base due to the rapid falloff of resolution with distance. In contrast, SM models are characterized by relatively uniform point densities controlled by camera optics, the numbers of images, and the distance from the target. This uniform density is the product of the Multiview stereo step in SM processing that fills areas between key points and is important for bedrock geologic mapping because it affords direct interpretation on a point cloud at a relatively uniform scale throughout a model. Our results indicate that these simple methods allow SM model construction to be accurate to the range of conventional GNSS with resolutions to the submeter, even cm, scale depending on data acquisition parameters. Thus, SM models can, and should, serve as a base for high-resolution geologic mapping, particularly in a steep terrain where conventional techniques fail. Our SM models appear to provide accurate visualizations of geologic features over km scales that allow detailed geologic mapping in 3D with a relative accuracy to the decimeter or centimeter level and absolute positioning in the 2–5 m precision of GNSS; a geometric precision that will allow unprecedented new studies of any geologic system where geometry is the fundamental data.
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Single Frame Lidar and Stereo Camera Calibration Using Registration of 3D Planes
This work focuses on finding the extrinsic parameters (rotation and translation) between the lidar and the stereo camera setups. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both the sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The planes extracted from the sensor’s data are used as reference data sets to find the relative transformation between the two sensors. We use our proposed method Correntropy Similarity Matrix Iterative Closest Point (CoSM-ICP) algorithm to estimate the relative transformation. In this work, we use a single frame of the point cloud data acquired from the lidar sensor and a single frame from the calibrated Stereo camera point cloud to perform this operation. We evaluate our approach on a simulated dataset since it has the freedom to evaluate under multiple configurations. Through results, we verify our approach under various configurations.
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- PAR ID:
- 10318770
- Date Published:
- Journal Name:
- 2021 Fifth IEEE International Conference on Robotic Computing (IRC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IRC52146.2021.00026
