Fast and Robust Iterative Closest Point

Zhang, Jie; Yao, Yuhang; Deng, Bailin

doi:10.1109/tpami.2021.3054619

Cited by 116 publications

(98 citation statements)

References 65 publications

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“…It is composed of observation equations implemented with Ceres solver [ 33 ]. It is based on the commonly used Iterative Closest Point procedure [ 34 , 35 , 36 ]. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors like in Chen et al [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Pełka¹,

Bedkowski

2021

Sensors

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This paper describes the calibration method for calculating parameters (position and orientation) of planar reflectors reshaping LiDAR’s (light detection and ranging) field of view. The calibration method is based on the reflection equation used in the ICP (Iterative Closest Point) optimization. A novel calibration process as the multi-view data registration scheme is proposed; therefore, the poses of the measurement instrument and parameters of planar reflectors are calculated simultaneously. The final metric measurement is more accurate compared with parameters retrieved from the mechanical design. Therefore, it is evident that the calibration process is required for affordable solutions where the mechanical design can differ from the inaccurate assembly. It is shown that the accuracy is less than 20 cm for almost all measurements preserving long-range capabilities. The experiment is performed based on Livox Mid-40 LiDAR augmented with six planar reflectors. The ground-truth data were collected using Z + F IMAGER 5010 3D Terrestrial Laser Scanner. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors. This work fulfils the gap between rotating and Solid-State LiDARs since the field of view can be reshaped by planar reflectors, and the proposed method can preserve the metric accuracy. Thus, such discussion concludes the findings. We prepared an open-source project and provided all the necessary data for reproducing the experiments. That includes: Complete open-source code, the mechanical design of reflector assembly and the dataset which was used in this paper.

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Section: Methodsmentioning

confidence: 99%

Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Pełka¹,

Bedkowski

2021

Sensors

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“…The red underline markers highlight the best case scenarios. Our method FGA is compuationally fast and robust compared to GA [11], BHRGA [12], FGR [13], DGR [14], DCP-v2 [15], FilterReg [16], PointNetLK [17], Fast Robust ICP [18], point-to-point ICP [6], RANSAC [19], GMMReg [20] and CPD [21].…”

Section: B Structure Of the Articlementioning

confidence: 99%

“…, B(X) using Eq. (18) compute N(Y), N(X) using Eq. (19) τ θ ← build BH tree on X with SPM S(X) using Alg.…”

Section: Algorithm 2: Fast Gravitational Approachmentioning

confidence: 99%

Fast Gravitational Approach for Rigid Point Set Registration With Ordinary Differential Equations

et al. 2021

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This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of n-body motion. Additional alignment cues can be integrated into FGA through particle masses. We propose a smooth-particle mass function for point mass initialization, which improves robustness to noise and structural discontinuities. To avoid the quadratic complexity of all-to-all point interactions, we adapt a Barnes-Hut tree for accelerated force computation and achieve quasilinear complexity. We show that the new method class has characteristics not found in previous alignment methods such as efficient handling of partial overlaps, inhomogeneous sampling densities, and coping with large point clouds with reduced runtime compared to the state of the art. Experiments show that our method performs on par with or outperforms all compared competing deep-learning-based and general-purpose techniques (which do not take training data) in resolving transformations for LiDAR data and gains state-of-the-art accuracy and speed when coping with different data.

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“…For efficiency, Bylow et al [18] have investigated the point-to-point metric and the point-to-plane metric. The Anderson acceleration strategy has been adopted to improve the convergence rate of the ICP algorithm [19,20]. For robustness against noise and outliers, researchers have developed numerous advanced methods based on many techniques, including the least trimmed squares [21], p sparsity optimization [22], nonconvex optimization [20,23], maximum correntropy criterion (MCC) [24], branch-and-bound scheme [25].…”

Section: Introductionmentioning

confidence: 99%

“…The Anderson acceleration strategy has been adopted to improve the convergence rate of the ICP algorithm [19,20]. For robustness against noise and outliers, researchers have developed numerous advanced methods based on many techniques, including the least trimmed squares [21], p sparsity optimization [22], nonconvex optimization [20,23], maximum correntropy criterion (MCC) [24], branch-and-bound scheme [25]. Recently, deep-learning-based registration methods [26][27][28][29][30] have demonstrated promising results.…”

Section: Introductionmentioning

confidence: 99%

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

Guo

et al. 2021

Remote Sensing

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Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes.

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Fast and Robust Iterative Closest Point

Cited by 116 publications

References 65 publications

Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Fast Gravitational Approach for Rigid Point Set Registration With Ordinary Differential Equations

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

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