Detecting keypoint sets on 3D point clouds via Histogram of Normal Orientations

Prakhya, Sai Manoj; Liu, Bingbing; Wang, Lin

doi:10.1016/j.patrec.2016.06.002

Cited by 20 publications

(9 citation statements)

References 23 publications

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“…This explicitly introduces the general assumption of a point-cloud registration problem; that the size of an overlapping area is very large and only a minor correction in translation and rotation is sought [4]. Generally, the solution in a high overlapping point-cloud consists of keypoints detection [7][8][9], descriptors calculation [10][11][12] around each of the keypoints and running an Iterative Closest Point (ICP) algorithm [13,14] to find a transformation that pair-wise matches the individual descriptors. When the overlapping area is small, as in our case, it is difficult to reliably find the matching keypoints in the two-point-clouds, which is an essential step in almost all of the existing point-cloud registration approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, we believe that a highly accurate line detector algorithm is an essential step to reduce the problem of determination of the σ parameter and, hence, generalize over wider variety of data. Moreover, as demonstrated in Equations (5) and (7) we used dynamic σ adjustment for each of the line pair by taking into account the distance from origins of the lines to their intersections on the quadratic surface constructed around A. This is a naive approach that we did not test extensively, but we empirically observed improvements in our results.…”

Section: Determining the Parameter σmentioning

confidence: 99%

“…This appendix section discusses details about functions nn y (·) and nn x (·) used in fitness functions (5) and (7) with the purpose of finding nearest neighboring intersection point on C in y and x directions respectively. Because of their usage in loop cycles, their implementation is critical for the overall speed of the algorithm.…”

Section: Appendix B Binary Nearest Neighbor Searchmentioning

confidence: 99%

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Low Overlapping Point Cloud Registration Using Line Features Detection

2019

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Modern robotic exploratory strategies assume multi-agent cooperation that raises a need for an effective exchange of acquired scans of the environment with the absence of a reliable global positioning system. In such situations, agents compare the scans of the outside world to determine if they overlap in some region, and if they do so, they determine the right matching between them. The process of matching multiple point-cloud scans is called point-cloud registration. Using the existing point-cloud registration approaches, a good match between any two-point-clouds is achieved if and only if there exists a large overlap between them, however, this limits the advantage of using multiple robots, for instance, for time-effective 3D mapping. Hence, a point-cloud registration approach is highly desirable if it can work with low overlapping scans. This work proposes a novel solution for the point-cloud registration problem with a very low overlapping area between the two scans. In doing so, no initial relative positions of the point-clouds are assumed. Most of the state-of-the-art point-cloud registration approaches iteratively match keypoints in the scans, which is computationally expensive. In contrast to the traditional approaches, a more efficient line-features-based point-cloud registration approach is proposed in this work. This approach, besides reducing the computational cost, avoids the problem of high false-positive rate of existing keypoint detection algorithms, which becomes especially significant in low overlapping point-cloud registration. The effectiveness of the proposed approach is demonstrated with the help of experiments.

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Section: Related Workmentioning

confidence: 99%

Section: Determining the Parameter σmentioning

confidence: 99%

Section: Appendix B Binary Nearest Neighbor Searchmentioning

confidence: 99%

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Low Overlapping Point Cloud Registration Using Line Features Detection

2019

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“…All of these techniques include keypoint recognition and their matching by employing element descriptors to find genuine keypoint correspondences. To find salient/interesting points on 3D point clouds, a variety of 3D keypoint detectors are available in the literature [5,6]. Following the detection of keypoints on 3D point clouds, feature descriptors are used to match them.…”

Section: Introductionmentioning

confidence: 99%

Learning Dense Features for Point Cloud Registration Using a Graph Attention Network

2022

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Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. Successful registration relies on extracting robust and discriminative geometric features. Though existing learning-based methods require high computing capacity for processing a large number of raw points at the same time, computational capacity limitation is not an issue thanks to powerful parallel computing process using GPU. In this paper, we introduce a framework that efficiently and economically extracts dense features using a graph attention network for point cloud matching and registration (DFGAT). The detector of the DFGAT is responsible for finding highly reliable key points in large raw data sets. The descriptor of the DFGAT takes these keypoints combined with their neighbors to extract invariant density features in preparation for the matching. The graph attention network (GAT) uses the attention mechanism that enriches the relationships between point clouds. Finally, we consider this as an optimal transport problem and use the Sinkhorn algorithm to find positive and negative matches. We perform thorough tests on the KITTI dataset and evaluate the effectiveness of this approach. The results show that this method with the efficiently compact keypoint selection and description can achieve the best performance matching metrics and reach the highest success ratio of 99.88% registration in comparison with other state-of-the-art approaches.

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“…In the literature [ 14 ], the future points are obtained via 3D Difference of Gaussians over geometric scalar values of the points which ensures obtaining salient features. Prakhya S M calculated the HoNo (Histogram of Normal Orientations) at every point and detected the key point by evaluating the properties of both the HoNo and the neighborhood covariance matrix [ 15 ]. The point feature histogram (PFH) algorithm and the fast point feature histogram (FPFH) algorithm are popular algorithms of feature description [ 16 – 18 ], which generate a feature histogram for each point based on feature information.…”

Section: Introductionmentioning

confidence: 99%

Automatic point cloud registration algorithm based on the feature histogram of local surface

Wang

Hua

et al. 2020

PLoS ONE

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In this paper, we present an efficient algorithm for point cloud registration in presence of low overlap rate and high noise. The proposed registration method mainly includes four parts: the loop voxel filtering, the curvature-based key point selection, the robust geometric descriptor, and the determining and optimization of correspondences based on key point spatial relationship. The loop voxel filtering filters point clouds to a specified resolution. We propose a key point selection algorithm which has a better anti-noise and fast ability. The feature descriptor of key points is highly exclusive which is based on the geometric relationship between the neighborhood points and the center of gravity of the neighborhood. The correspondences in the pair of two point clouds are determined according to the combined features of key points. Finally, the singular value decomposition and ICP algorithm are applied to align two point clouds. The proposed registration method can accurately and quickly register point clouds of different resolutions in noisy situations. We validate our proposal by presenting a quantitative experimental comparison with state-of-the-art methods. Experimental results show that the proposed point cloud registration algorithm has faster calculation speed, higher registration accuracy, and better anti-noise performance.

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Detecting keypoint sets on 3D point clouds via Histogram of Normal Orientations

Cited by 20 publications

References 23 publications

Low Overlapping Point Cloud Registration Using Line Features Detection

Low Overlapping Point Cloud Registration Using Line Features Detection

Learning Dense Features for Point Cloud Registration Using a Graph Attention Network

Automatic point cloud registration algorithm based on the feature histogram of local surface

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