Comparing Seven Methodogies for Rigid Alignment of Point Clouds with Focus on Frame-to-Frame Registration in Depth Sequences

Abstract

Pairwise rigid registration aims to find the rigid transformation that best registers two surfaces represented by point clouds. This work presents a comparison between seven algorithms, with different strategies to tackle rigid registration tasks. We focus on the frame-to-frame problem, in which the point clouds are extracted from a video sequence with depth information generating partial overlapping 3D data. We use both point clouds and RGB-D video streams in the experimental results. The former is considered under different viewpoints with the addition of a case-study simulating missing data. Since the ground truth rotation is provided, we discuss four different metrics to measure the rotation error in this case. Among the seven considered techniques, the Sparse ICP and Sparse ICP-CTSF outperform the other five ones in the point cloud registration experiments without considering incomplete data. However, the evaluation facing missing data indicates sensitivity for these methods against this problem and favors ICP-CTSF in such situations. In the tests with video sequences, the depth information is segmented in the first step, to get the target region. Next, the registration algorithms are applied and the average root mean squared error, rotation and translation errors are computed. Besides, we analyze the robustness of the algorithms against spatial and temporal sampling rates. We conclude from the experiments using a depth video sequences that ICP-CTSF is the best technique for frame-to-frame registration.

Reverse Engineering: Investigation of Optimization Techniques in Point Clouds Registration

“…The PCR process can be formulated as an optimization problem, and the objective is to find the optimum transformation that aligns the two point clouds with each other [2,3]. Many contributions have been proposed to tackle the challenging point cloud registration problem [4,5], based on techniques such as Iterative Closest Point (ICP) [6], improved ICP algorithm [7,8], geometric histograms [9] and LiDAR registration methods [10]. Bels and McKay proposed the Iterative Closest Point (ICP) algorithm [6].…”