Abstract
Three-dimensional (3-D) point clouds are widely considered for applications in different fields. Various methods have been proposed to generate point cloud data: LIDAR and image matching from static and mobile platforms, including, e.g., terrestrial laser scanning. With multiple point clouds from stationary platforms, point cloud registration is a crucial and fundamental issue. A standard approach is a point-based registration, which relies on pairs of corresponding points in two-point clouds. Therefore, a necessary step in point-based registration is the construction of 3-D local descriptors. One of the (many) challenges that will specifically affect the performance of local descriptors with local spatial information is the point displacement error. This error is caused by the difference in the distributions of points surrounding a (potentially) corresponding center point in the two-point clouds. It can occur for various reasons such as 1) distortions caused by the sensors recording the data, 2) moving objects, 3) varying density of point cloud, 4) change of viewing angle, and 5) different of the sensors. The purpose of this article is to develop a new 3-D local descriptor reducing the effect of this type of error in point cloud coarse registration. The approach includes an improved local reference frame and a new geometric arrangement in point cloud space for the 3-D local descriptor. Inspired by the 2-D DAISY descriptor, a geometric arrangement is created to reduce the effect of the point displacement error. in addition, directional histograms are considered as features. Investigations are performed for point clouds from challenging environments, which are publicly available. The results of this study show the high performance of the proposed approach for point cloud registration, especially in more challenging and noisy environments.
Highlights
3D point clouds are widely considered for applications in different fields
This paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE
The main goal of this paper is to provide an accurate and stable framework for large-scale point cloud registration to reduce the effect of the point displacement error
Summary
One of the (many) challenges that will affect the performance of local descriptors with local spatial information is the point displacement error This error is caused by the difference in the distributions of points surrounding a (potentially) corresponding center point in the two-point clouds. The purpose of this article is to develop a new 3D local descriptor reducing the effect of this type of error in point cloud coarse registration. Point clouds registration methods can be divided into three categories: 1-greedy searching-based [6], [7], 2-global featurebased [8], [9], and 3-local feature-based [10]-[13]. Local features are more suitable for aligning point clouds which partially overlap Both global and local feature-based methods consist of two parts, coarse and fine registration [14].
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