SAC-COT:基于样本兼容三角形的3D点云定位鲁棒估计方法
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立即下载 SAC-COT（Sample Consensus by Sampling Compatibility Triangles）是一项针对3D点云注册问题的创新算法，发表在《IEEE Transactions on Geoscience and Remote Sensing》期刊的一篇论文中。该方法在6-DOF（六自由度）姿态估计领域具有显著价值，特别是在处理初始对应关系中可能存在大量噪声或异常值的情况下。传统上，通过特征对应关系进行6-DOF定位是3D registration中的一个常用且稳定的策略，但处理异常数据的挑战仍然存在。
SAC-COT的核心贡献在于提出了一种基于样本的兼容性三角形（Compatibility Triangle, COT）的引导性三点采样方法。不同于传统的对应点表示，COT是一种新的数据结构，它代表了三个相互兼容的点之间的关系，这些点共同构成一个有效的几何约束。首先，作者将对应关系集建模为图，其中节点连接着兼容的对应点对，形成一个图结构。
算法的关键步骤包括：
1. 模型构建：利用COT作为节点间的连接，构建一个图，图中边的权重可能反映了对应点之间匹配的可靠性。
2. COT排序与筛选：通过某种排序策略（如基于COT的质量评估或基于图的排序算法），对图中的COT进行优先级排序，确保在处理过程中优先考虑最可靠的匹配。
3. 采样策略：通过采样高质量的COT，形成一个小型的样本集，这有助于减少噪声和异常的影响。采样过程可能涉及到局部搜索、随机抽样或基于置信度的选择。
4. 一致性检验：对采样的COT进行一致性检查，如果它们可以构成一个稳定的三角形系统，那么就可以推断出相对应的6-DOF姿态估计。
5. 迭代优化：基于样本的共识，可能需要多次迭代来逐步改进估计结果，直到达到预设的精度标准或达到收敛。
6. 鲁棒性和效率：SAC-COT的优势在于其简单易实现且有效，能够在保持高精度的同时抵抗大规模的噪声，提高了3D点云注册的鲁棒性和效率。
通过SAC-COT，研究者们能够处理初始对应关系中的噪声挑战，从而实现更准确和稳健的3D点云配准，这对于许多应用，如遥感、机器人导航、虚拟现实等领域都具有重要的实际意义。
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
YANG et al.: SAC-COT FOR 3-D POINT CLOUD REGISTRATION 3
method. Section IV presents the deployed experiments to
validate the effectiveness of our method. Finally, Section V
concludes this article and presents future research directions.
II. R
ELATED WORK
This section first gives a brief review on 6-DOF pose estima-
tors, geometrics constraints, and deep learning techniques for
3-D registration. Then, since SAC-COT is a guided sampling-
based estimator, existing guided sampling approaches in the
RANSAC family is recapped.
A. 6-DOF Pose Estimators for 3-D Registration
For the problem of estimating a 6-DOF pose from cor-
respondences with outliers in the context of 3-D regis-
tration, RANSAC [15] and its variants remain the main-
stream solution. The classical RANSAC exhibits two main
limitations when faced with heavy outliers: low time effi-
ciency and limited accuracy. To overcome the two limita-
tions, Rusu et al. [10] presented a sample consensus-based
initial alignment (SAC-IA) method, which samples corre-
spondences spread out on the point cloud and leverages the
Huber penalty for hypothesis evaluation. To achieve more
reliable hypothesis evaluation, Yang et al. [7] proposed a point
cloud distance metric to assess pose hypotheses and intro-
duced the optimal sample consensus (OSAC) method. Both
SAC-IA and OSAC are three-point-based sampling methods
that exhibit a theoretical computational complexity of O(n
3
).
Some methods that sample fewer correspondences per itera-
tion have also been proposed. For instance, two-point-based
sample consensus with global constraint (2SAC-GC) [21] and
compatibility-guided sample consensus (CG-SAC) [1] addi-
tionally consider the normal information of keypoints during
the sampling process; globally constrained one-point-based
sample consensus (GC1SAC) [17] and one-point RANSAC
(1P-RANSAC) [16] additionally employ the LRF cue and only
sample one correspondence per iteration. Unfortunately, these
RANSAC methods still fail to achieve a good balance in terms
of accuracy, speed, and robustness to common nuisances (as
will be verified in Section IV).
In addition to RANSAC methods, Guo et al. [20] proposed a
clustering-based method that first generates pose hypotheses in
the rotation and translation spaces and then locates the cluster
centers in both spaces; Tombari and Di Stefano [18] and Buch
et al. [19] proposed two voting-based methods that serve pose
hypotheses as voters and perform voting in a Hough space. All
these methods are dependent on the normals or LRFs, which
have been demonstrated to be instable in the presence of noise,
partial overlap, and occlusions [13], [27], [28].
B. Geometric Constraints for 3-D Registration
Point-, plane-, and correspondence-level geometric con-
straints have been proposed to assist 3-D registration. For
point-level constraints, Aiger et al. [29] enforced coplanar
constraints to four-point sets in point clouds to achieve reg-
istration; Drost et al. [30] presented a point pair voting-
based method for 3-D registration, where distance and angle
constraints are employed to describe point pair features. For
plane-level constraints, Xu et al. [31] introduced a voxel-
based four-plane congruent set (V4PCS) method for pair-
wise coarse registration, which first extracts the planes for
point clouds and then leverages the plane-based geometric
constraints formed by four planes to achieve registration;
Chen et al. [32] presented a plane-based descriptor using dis-
tance and angle properties derived from planes for 3-D regis-
tration. For correspondence-level geometric constraints, most
of the existing methods focus on pairwise constraints. They
typically include the L
2
distance constraint [22], [33], normal
deviation angle constraint [21], [34], and LRF constraint [35].
Nonetheless, these pairwise constraints either suffer from
ambiguities or lack robustness to common nuisances. The
readers can refer to a related survey [36] for terrestrial laser
scanner point cloud registration for more details on geometrics
constraints proposed for 3-D registration.
SAC-COT leverages the correspondence-level geometric
constraints for registration, while it proposes a novel triplet
constraint that is validated to be far less ambiguous and more
robust than existing pairwise constraints.
C. Deep Learning for 3-D Registration
In addition to geometric-only methods, a few deep learning-
based point cloud registration methods have been pro-
posed recently. We review some typical examples here.
Aoki et al. [37] proposed the PointNetLK network for 3-D
registration in an iterative manner, where PointNet [38]
was employed for descriptor extraction combined with
a Lucas/Kanade-like optimization algorithm. Wang and
Solomon [39] presented deep closest point (DCP) that incor-
porates a point cloud embedding network, an attention-based
module to approximate combinatorial matching, and a differ-
entiable singular value decomposition (SVD) layer for 6-DOF
pose estimation. PRNet [40], as an extension to DCP, specifi-
cally focuses on partial-to-partial registration, which contains
a keypoint detection module and is able to establish partial
correspondences.
Deep learning techniques have demonstrated a great poten-
tial for 3-D registration, while usually, adequate labeled train-
ing data should be prepared. This may not be feasible in many
practical scenarios, and as such, we still focus geometric-only
methods.
D. Guided Sampling Approaches for RANSAC
As the key novelty of SAC-COT is its sampling method,
we herein review existing guided sampling methods for
RANSAC. In the 2-D domain, guided sampling approaches
preferentially sample correspondences with high confidence
scores [26], [41]. By contrast, guided sampling approaches
for RANSAC in the 3-D domain, as will be discussed in the
following, are more diverse.
For one-point-based sampling methods [16], [17], because
they have a linear time complexity, guided or random sampling
ways generate the same outcome as long as all correspon-
dences have been traversed. However, these one-point-based
sampling methods typically guide the sampling process based
Authorized licensed use limited to: FUDAN UNIVERSITY. Downloaded on March 14,2021 at 06:43:52 UTC from IEEE Xplore. Restrictions apply.
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