The PCL Registration API

1. 개요

• 정의 : The problem of consistently aligning various 3D point cloud data views into a complete model is known as registration
• 목적 : find the relative positions and orientations of the separately acquired views in a global coordinate framework
• 분류
  • Global Registration : 초기값 불필요
  • Local Registration : 초기값 필요(Global Regstration사용), eg. ICP registration
• 절차 : subsequent processing steps such as segmentation and object reconstruction can be applied.
• The algorithmic work in the PCL registration library is motivated by
  • 대응점 찾기 finding correct point correspondences in the given input datasets,
  • 강체 변환(회전, 이동) 예측 and estimating rigid transformations that can rotate and translate each individual dataset into a consistent global coordinate framework.
• 필요 기능 PCL contains a set of powerful algorithms that allow
  • 대응점 평가 방법the estimation of multiple sets of correspondences,
  • 나쁜 대응점 제거 방 as well as methods for rejecting bad correspondences,
  • 변환 파악 and estimating transformations in a robust manner from them.

2. An overview of pairwise registration

• 목표 : transformation matrix (4x4) 계산
  • representing the rotation and translation

절차 The computational steps for two datasets are straightforward:

• from a set of points, identify interest points (i.e., keypoints) that best represent the scene in both datasets;
• at each keypoint, compute a feature descriptor;
• from the set of feature descriptors together with their XYZ positions in the two datasets,
  • estimate a set of correspondences, based on the similarities between features and positions;
• given that the data is assumed to be noisy, not all correspondences are valid, so reject those bad correspondences that contribute negatively to the registration process;
• from the remaining set of good correspondences, estimate a motion transformation.

3. Registration modules

3.1 Keypoints

keypoint is an interest point that has a “special property” in the scene

• 코너

There are a number of different keypoints available in PCL like

• NARF
• SIFT
• FAST

3.2 Feature descriptors

Based on the keypoints found we have to extract features

Again there is a number of feature options to choose from, for example

• NARF
• FPFH
• BRIEF
• SIFT

3.3 Correspondences estimation

Depending on the feature type we can use different methods to find the correspondences.

A. For point matching

using the points’ xyz-coordinates as features

• brute force matching,
• kd-tree nearest neighbor search (FLANN),
• searching in the image space of organized data, and
• searching in the index space of organized data.

B. For feature matching

not using the points’ coordinates, but certain features

• brute force matching and
• kd-tree nearest neighbor search (FLANN).

3.4 Correspondences rejection

Since wrong correspondences can negatively affect the estimation of the final transformation, they need to be rejected.

방법

• RANSAC
• trimming down the amount and using only a certain percent of the found correspondences

3.5 Transformation estimation

compute the transformation.

• evaluate some error metric based on correspondence
• estimate a (rigid) transformation between camera poses (motion estimate) and minimize error metric
• optimize the structure of the points
  • Examples: - SVD for motion estimate; - Levenberg-Marquardt with different kernels for motion estimate;
• use the rigid transformation to rotate/translate the source onto the target, and potentially run an internal ICP loop with either all points or a subset of points or the keypoints
• iterate until some convergence criterion is met

• eg. Normal Distributions Transform (NDT)

4. Example pipelines

4.1 Iterative Closest Point

• Search for correspondences.
• Reject bad correspondences.
• Estimate a transformation using the good correspondences.
• Iterate.

4.2 Feature based registration

• use SIFT Keypoints (pcl::SIFT…something)
• use FPFH descriptors (pcl::FPFHEstimation) at the keypoints
  • see our tutorials for that, like
• get the FPFH descriptors and estimate correspondences using pcl::CorrespondenceEstimation
• reject bad correspondences using one or many of the pcl::CorrespondenceRejectionXXX methods
• finally get a transformation as mentioned above