Robust 3D Line Extraction from Stereo Point Clouds

Lu, Zhaojin; Baek, Seung-Min; Lee, Sukhan

doi:10.1109/ramech.2008.4681439

Cited by 21 publications

(6 citation statements)

References 11 publications

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“…The authors defined the 3D edges as "3D discontinuities of the geometric properties in the underlying 3D-scene". They combined the RANdom SAmple Consensus (RANSAC) [54] and angular gap metric [55] to extract edge feature points. This method can extract two kinds of feature points, i.e., boundary elements and fold edges, which actually include all types of edges in a point cloud data.…”

Section: Feature Point Extractionmentioning

confidence: 99%

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

et al. 2021

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Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.

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Section: Feature Point Extractionmentioning

confidence: 99%

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

et al. 2021

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“…For registering point clouds captured on dynamic platforms, the coarse alignment process is usually replaced by the introduction of hardware sensors, namely GNSS receivers and IMUs [53]. Points [35,[54][55][56][57][58][59][60][61][62], lines [63][64][65][66], planes [23,47,[67][68][69][70][71], voxels [72,73], and the selections of them [19,[74][75][76] are used to recover the positioning and orienting changes in the dynamic process and achieve fine registration. Since the limited number of planes extracted and the poor quality of planes may result in failures in aligning low-resolution single-frame point clouds, a highly robust and reliable plane extraction method is required for highly dynamic mobile mapping in indoor environments.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System

Fan¹,

Shi²,

Xiang³

et al. 2019

Remote Sensing

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Over the last decade, increasing demands for building interior mapping have brought the challenge of effectively and efficiently acquiring geometric information. Most mobile mapping methods rely on the integration of Simultaneous Localization And Mapping (SLAM) and costly Inertial Measurement Units (IMUs). Meanwhile, the methods also suffer misalignment errors caused by the low-resolution inhomogeneous point clouds captured using multi-line Mobile Laser Scanners (MLSs). While point-based alignments between such point clouds are affected by the highly dynamic moving patterns of the platform, plane-based methods are limited by the poor quality of the planes extracted, which reduce the methods’ robustness, reliability, and applicability. To alleviate these issues, we proposed and developed a method for plane extraction from low-resolution inhomogeneous point clouds. Based on the definition of virtual scanlines and the Enhanced Line Simplification (ELS) algorithm, the method extracts feature points, generates line segments, forms patches, and merges multi-direction fractions to form planes. The proposed method reduces the over-segmentation fractions caused by measurement noise and scanline curvature. A dedicated plane-to-plane point cloud alignment workflow based on the proposed plane extraction method was created to demonstrate the method’s application. The implementation of the coarse-to-fine procedure and the shortest-path initialization strategy eliminates the necessity of IMUs in mobile mapping. A mobile mapping prototype was designed to test the performance of the proposed methods. The results show that the proposed workflow and hardware system achieves centimeter-level accuracy, which suggests that it can be applied to mobile mapping and sensor fusion.

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“…However, a 2D line segment contains multiple depth samples across its length, which suggests that one should exploit them instead of simply back-projecting the two depth pixels corresponding to the line endpoints as these are noisy, may be missing or correspond instead to the background, for lines tend to be detected on the object contours where depth is discontinuous. Hence, to obtain the 3D lines we propose to use the robust method of [6] with two simple modifications to address the computational cost of the original framework: 1) Using the Euclidean point-to-line distance within the RANSAC outlier filtering instead of the originally proposed Mahalanobis distance, for the two metric have not shown significant differences in terms of pose estimation accuracy, in our experiments (Section IV), however the computation of the Mahalanobis distance requires either inverting the uncertainty matrices of the line depth samples or applying whitening transformations to them [17]. In our implementation, using Eigen library, computing the Mahalanobis distances makes the process coarsely 3 times slower, thus we resort to use simply the Euclidean distance with an inlier threshold of 3 cm.…”

Section: Point Depth Sampling and 3d Line Fittingmentioning

confidence: 99%

SPLODE: Semi-probabilistic point and line odometry with depth estimation from RGB-D camera motion

Proença

Gao

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Active depth cameras suffer from several limitations, which cause incomplete and noisy depth maps, and may consequently affect the performance of RGB-D Odometry. To address this issue, this paper presents a visual odometry method based on point and line features that leverages both measurements from a depth sensor and depth estimates from camera motion. Depth estimates are generated continuously by a probabilistic depth estimation framework for both types of features to compensate for the lack of depth measurements and inaccurate feature depth associations. The framework models explicitly the uncertainty of triangulating depth from both point and line observations to validate and obtain precise estimates.Furthermore, depth measurements are exploited by propagating them through a depth map registration module and using a frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D reprojection errors, independently. Results on RGB-D sequences captured on large indoor and outdoor scenes, where depth sensor limitations are critical, show that the combination of depth measurements and estimates through our approach is able to overcome the absence and inaccuracy of depth measurements.

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Robust 3D Line Extraction from Stereo Point Clouds

Cited by 21 publications

References 11 publications

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System

SPLODE: Semi-probabilistic point and line odometry with depth estimation from RGB-D camera motion

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