本文為PCL官方教程的Registration模塊的中文簡介版。

An Overview of Pairwise Registration

點云配準包括以下步驟：

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.

針對上述每一個步驟，PCL的registration模塊提供了多種算法進行實現 。

Keypoint

諸如?NARF, SIFT and FAST。

Feature descriptors

諸如NARF, FPFH, BRIEF or SIFT。

Correspondences Estimation

point matching

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.

feature matching

brute force matching and

kd-tree nearest neighbor search (FLANN).

Corresdondences Rejection

使用RANSAC，或者剪出多余數據。

Transformation Estimation

諸如?SVD for motion estimate; - Levenberg-Marquardt with different kernels for motion estimate。

算法案例

其中(1)和(2)是point matching，(3)是feature matching。

(1)

ICP的使用SVD求解轉換矩陣，其參考文章：

Least-Squares Estimation of Transformation Parameters Between Two Point Patterns

(2)

參考論文：

1. The Three-Dimensional Normal-Distributions Transform?an Efficient Representation for Registration,?Surface Analysis, and Loop Detection. MARTIN MAGNUSSON doctoral dissertation。

2. Line Search Algorithm with Guaranteed Sufficient Decrease. 計算迭代步長。

參考論文：

Pose Estimation using Local Structure-Specific Shape and Appearance Context. ICRA 2013.

相關資料：

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