Surface Reconstruction for Ground Map Generation in Autonomous Excavation

Excavator’s main tasks include digging, trenching, and ground leveling at construction sites, as well as work efficiency and safety can be improved by using an autonomous excavator. A prerequisite step to achieving an autonomous excavation is to obtain a sound perception of the surrounding ground. For this, a LiDAR sensor has been widely used to scan the environment. However, the point cloud generated by the LiDAR is not ideal for surface reconstruction to generate a ground map, as it suffers from flaws such as noise and outlier points. To tackle this issue, our paper proposes advanced methodologies for surface reconstruction algorithms.

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Surface Reconstruction from Structured Light Images Using Differentiable Rendering

Sensors ◽

10.3390/s21041068 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1068

Author(s):

Janus Nørtoft Jensen ◽

Morten Hannemose ◽

J. Andreas Bærentzen ◽

Jakob Wilm ◽

Jeppe Revall Frisvad ◽

...

Keyword(s):

Surface Reconstruction ◽

Point Cloud ◽

Structured Light ◽

Reconstruction Algorithms ◽

Triangle Mesh ◽

Intermediate Point ◽

Structured Light Scanning ◽

Continuous Surface ◽

One Step ◽

Sharp Edges

When 3D scanning objects, the objective is usually to obtain a continuous surface. However, most surface scanning methods, such as structured light scanning, yield a point cloud. Obtaining a continuous surface from a point cloud requires a subsequent surface reconstruction step, which is directly affected by any error from the computation of the point cloud. In this work, we propose a one-step approach in which we compute the surface directly from structured light images. Our method minimizes the least-squares error between photographs and renderings of a triangle mesh, where the vertex positions of the mesh are the parameters of the minimization problem. To ensure fast iterations during optimization, we use differentiable rendering, which computes images and gradients in a single pass. We present simulation experiments demonstrating that our method for computing a triangle mesh has several advantages over approaches that rely on an intermediate point cloud. Our method can produce accurate reconstructions when initializing the optimization from a sphere. We also show that our method is good at reconstructing sharp edges and that it is robust with respect to image noise. In addition, our method can improve the output from other reconstruction algorithms if we use these for initialization.

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Direct rapid prototyping from point cloud data without surface reconstruction

Computer-Aided Design and Applications ◽

10.1080/16864360.2017.1397889 ◽

2017 ◽

Vol 15 (3) ◽

pp. 390-398

Author(s):

Tianyun Yuan ◽

Xiaobo Peng ◽

Dongdong Zhang

Keyword(s):

Rapid Prototyping ◽

Surface Reconstruction ◽

Point Cloud ◽

Point Cloud Data ◽

Cloud Data

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Performance Analysis of Surface Reconstruction Algorithms in Vertical Scanning Interferometry Based on Coherence Envelope Detection

Micromachines ◽

10.3390/mi12020164 ◽

2021 ◽

Vol 12 (2) ◽

pp. 164

Author(s):

Dongxu Wu ◽

Fusheng Liang ◽

Chengwei Kang ◽

Fengzhou Fang

Keyword(s):

Surface Reconstruction ◽

Measurement Accuracy ◽

Experimental Studies ◽

Reconstruction Algorithm ◽

Low Noise ◽

Surface Measurement ◽

Reconstruction Algorithms ◽

Centroid Method ◽

Envelope Detection ◽

Vertical Scanning Interferometry

Optical interferometry plays an important role in the topographical surface measurement and characterization in precision/ultra-precision manufacturing. An appropriate surface reconstruction algorithm is essential in obtaining accurate topography information from the digitized interferograms. However, the performance of a surface reconstruction algorithm in interferometric measurements is influenced by environmental disturbances and system noise. This paper presents a comparative analysis of three algorithms commonly used for coherence envelope detection in vertical scanning interferometry, including the centroid method, fast Fourier transform (FFT), and Hilbert transform (HT). Numerical analysis and experimental studies were carried out to evaluate the performance of different envelope detection algorithms in terms of measurement accuracy, speed, and noise resistance. Step height standards were measured using a developed interferometer and the step profiles were reconstructed by different algorithms. The results show that the centroid method has a higher measurement speed than the FFT and HT methods, but it can only provide acceptable measurement accuracy at a low noise level. The FFT and HT methods outperform the centroid method in terms of noise immunity and measurement accuracy. Even if the FFT and HT methods provide similar measurement accuracy, the HT method has a superior measurement speed compared to the FFT method.

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Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030157 ◽

2021 ◽

Vol 10 (3) ◽

pp. 157

Author(s):

Paul-Mark DiFrancesco ◽

David A. Bonneau ◽

D. Jean Hutchinson

Keyword(s):

Computational Geometry ◽

Surface Reconstruction ◽

Power Law ◽

Point Cloud ◽

Point Clouds ◽

Volume Estimation ◽

Estimation Methods ◽

Small Scale ◽

Reconstruction Methods ◽

3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

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