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Contextual Global Registration of Point Clouds in Urban Scenes

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Contextual Global Registration of Point Clouds in Urban Scenes Contextual Global Registration of Point Clouds in Urban Scenes Xuming Ge and Bo Wu Abstract a refined strategy with a local registration algorithm such as This paper presents an innovative and powerful approach iterative closest point (ICP) (Besl and McKay 1992) can be for contextual global registration of partially overlapping executed to arrive at a local optimal alignment. point clouds in urban scenes. First, a fast and robust strat- Exploitation of key-points to represent the scanning scene egy for extraction of feature points from large-scale scan- is necessary for registration of large-scale point clouds. Most ning scenes is proposed and used to represent the scan- registration methods typically apply general detectors such as ning scene. Correspondences are then established using a intrinsic shape signature (Zhong 2009) and 3D Harris (Sipiran contextual rule-based method at the two-dimensional and and Bustos 2011), but they have their drawbacks. First, the key- three-dimensional levels. A penalization strategy is then points extracted from such general detectors do not contain introduced to generate dense corresponding sets between specific geometric meaning that reduces the repeatability of two overlapping point clouds. Finally, a three-stage optimal the same key-point from various scanning scenes. In addition, strategy is implemented to efficiently match these point the quantity and quality of the key-points obtained from those clouds. The proposed approach highlights the following detectors depend largely upon the window size for searching. aspects: (1) The designed voting-based feature extraction Contextual information is widely concerned in the clas- method can efficiently and robustly represent a scanning sification of point clouds (Niemeyeret al. 2016; Vosselman et scene in a complex environment with a huge database; (2) al. 2017). One of the most significant properties that sup- The contextual information enables the generation of more ports those applications is a certain spatial continuity shown reliable correspondences beyond the geometric features; (3) by most scanned objects, hence the quantified feature also The novel penalization strategy and the three-stage optimal shows a certain degree of homogeneity and continuity. The method benefit the approach to achieve tight alignment at bottleneck, however, in introducing such strategy to generate a lower computational cost. State-of-art baseline algorithms a descriptor into point cloud registration is that contextual from the field of photogrammetry are used to evaluate the information is typically supported by a neighborhood. It is performance of the proposed method in the comprehensive distinctiveness for 3D key points in most urban environments experiments. The presented approach outperforms other which is dominated by planar structure. methods in registration accuracy and computational cost. For matching of the correspondences candidate, the itera- tive process is typically based on variants of RANSAC. An op- Delivered by Ingenta timal strategy typically based on the least-squares (LS) theory IP: 192.168.39.211 On: Thu, 30 Sep 2021 07:45:02 Introduction is applied to optimize an objective with the selected samples The registration is particularly challengingCopyright: when American the pairwise Society forfrom Photogrammetry a set of candidate and correspondences. Remote Sensing On one hand, be- scanning scenes only partially overlap and when no initial cause RANSAC is a statistical and probabilistic approach, many alignment values are given. Point cloud registration should iterations are required, which makes estimation and valida- overcome three key challenges: (1) the huge amount of data tion both computationally expensive. On the other hand, the (Theiler et al. 2014), (2) the strong variations in point densi- use of LS to realize optimization is positively related to the ties caused by the line-of-sight scanning principle in long-dis- redundancy, and LS does not contain the robust property. That tance measurements (Dong et al. 2018), and (3) the large num- means that the increasing redundancy or observations can bers of outliers and noise in complex scanning scenes (Zai improve the quality of the estimations, but with this benefit et al. 2017). To overcome these challenges, extensive studies comes an increased likelihood of risk (i.e., increasing the have been performed to extend the original registration strate- iterations or trapping the convergence). gies or design novel frameworks to strengthen the capabilities To address the aforementioned issues, this paper presents of these approaches. To address a large-scale scene with a an innovative and powerful global registration method in huge number of points and partial overlap, practical registra- which the correspondences will be established more effective tion pipelines use key-point detectors (e.g., Harris three-di- based on the robust key-points; what is more, a novel iterative mensional (3D) (Sipiran and Bustos 2011)) to extract interest- strategy is introduced to improve the registration accuracy ing points and then use feature descriptors such as fast point and the computational cost. feature histograms (FPFH) (Rusu et al. 2009) to describe such points; the correspondences can then be established upon a specific rule (e.g., the nearest-neighborhood rule (Besl and Related Work McKay 1992)) and then with the iterative model fitting frame- The registration of point clouds has been extensively studied work such as random sample consensus (RANSAC) (Bolles (Salvi et al. 2007; Tam et al. 2013). The goal of coarse registra- and Fischler 1981). For each iteration, the program exploits tion is to identify a high-quality initial alignment between a set of candidate correspondences or its samples, produces adjacent point clouds with arbitrary position and orientation; an alignment based on those correspondences, and carries out a validation process. If sufficient accuracy is established, Photogrammetric Engineering & Remote Sensing Vol. 85, No. 8, August 2019, pp. 559–571. 0099-1112/19/559–571 Department of Land Surveying & Geo-Informatics, The Hong © 2019 American Society for Photogrammetry Kong Polytechnic University ([email protected]). and Remote Sensing doi: 10.14358/PERS.85.8.559 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING August 2019 559 coarse registration is thus a global strategy (Zhou et al. 2016). (1) We present an effective and robust voting-based method to A common registration method should address the following extract key-points that will assess each point with two-di- issues: (1) representation of scanning scenes with necessary mensional (2D) information. The reduced-order processing points, (2) extraction of key-points and description of points can accelerate the speed of calculation. And for screening with discriminative features, and (3) establishment of cor- via the voting mechanism, each key-point contains spe- respondences and application of an iterative model fitting cific geometric information that improves its repeatability framework to align adjacent point clouds. Each of these stages from adjacent point clouds. are reviewed in turn. (2) We propose a quick and simple approach to apply contex- Most point cloud registration methods operate by down- tual information in registration such that, apart from the sampling point clouds. The common method is to use a filter, geometric features, contextual information is also used to such as a voxel-grid, to down-sample the point clouds (Holz establish and purify the correspondences between adja- et al. 2015). The extraction of geometric primitives such as cent point clouds. lines (Stamos and Leordeanu 2003) and planes (Dold and Brenner 2006) to represent the scanning scene is also an (3) We provide a penalization strategy to reduce the costs effective approach to simplify a complex scanning scene. of calculation in both the iterations and the validations. Researchers currently use super-voxels for simplification Moreover, the quality of the estimates can be improved by and regularization of point clouds (Xu et al. 2017; Zhu et al. applying such a strategy with reasonable weighting. 2017) that inherit the reliability and efficiency of the octree. (4) We present a novel iterative fitting framework to imple- Another method used to simplify a scanning scene is by cross ment the iterative estimation, which improves perfor- section point clouds (Yang et al. 2016; Ge 2017). Two advan- mance both in registration accuracy and in computational tages of this method are that the continuous features on an ob- costs by changing the data dimension during the itera- ject’s surface can be preserved as completely as possible and tions. the number of point clouds can be greatly reduced without a high computation cost. Generally, most registration methods are used to locate Contextual Global Registration of Point Clouds key-points by using various detectors and to describe those Overview of the Approach key-points with various feature descriptors. Many detectors, The proposed CGR approach is an efficient and robust global including intrinsic shape signature (Zhong 2009), Harris 3D registration method used to solve large-scale point cloud (Sipiran and Bustos 2011), 3D SURF (Knopp et al. 2010), and registration issues in photogrammetry. CGR extends the poten- 2.5D SIFT (Lo and Siebert 2009), can be used directly with tial of the coarse registration method 4PCS (Aiger et al. 2008; point clouds to extract the corresponding key-points (Theiler Mellado et
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