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feature_n_slam

Mar 11, 2026, 3 min read

特征与SLAM

基于传统方法的特征提取

特征点

The main differences between the PFH and FPFH formulations are summarized below:
1.  the FPFH does not fully interconnect all neighbors of ![p_q](https://pcl.readthedocs.io/projects/tutorials/en/latest/_images/math/1557bf30f68d8d9460f124be9bad1e7739a98601.png) as it can be seen from the figure, and is thus missing some value pairs which might contribute to capture the geometry around the query point;
2.  the PFH models a precisely determined surface around the query point, while the FPFH includes additional point pairs outside the **r** radius sphere (though at most **2r** away);
3.  because of the re-weighting scheme, the FPFH combines SPFH values and recaptures some of the point neighboring value pairs;
4.  the overall complexity of FPFH is greatly reduced, thus making possible to use it in real-time applications;
5.  the resultant histogram is simplified by decorrelating the values, that is simply creating _d_ separate feature histograms, one for each feature dimension, and concatenate them together (see figure below).

边缘

边缘检测Edge detection

Canny
LoG和DoG

Edge linking

线的检测

霍夫变换Hough transforms

特征检测

哈尔级联Haar Cascades

  • 主要用于人脸检测？

尺度不变特征变换SIFT与加速鲁棒特征SURF

  •  SIFT - ScaleInvariant Feature Transform
  • SURF - Speeded Up Robust Feature
    • SIFT的加速版
    • 尺度不变

但是SIFT相对于SURF的优点就是，由于SIFT基于浮点内核计算特征点，因此通常认为， SIFT算法检测的特征在空间和尺度上定位更加精确，所以在要求匹配极度精准且不考虑匹配速度的场合可以考虑使用SIFT算法。

二级制鲁棒独立基本特征BRIEF与旋转的BRIEF——ORB

ORB没有解决尺度不变性

FAST

  • 加速分割测试获得特征， Features from Accelerated Segment Test
  • 速度快

哈里斯角点检测Harris

iterative algorithms

  • iterative algorithms
    • ICP， Go-ICP
  • RANSAC
    • RANdom SAmple Consensus (Fischler and Bolles 1981, Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography)

      根据一组包含异常数据的样本数据集，计算出数据的数学模型参数，得到有效样本数据的算法

基于机器学习的特征提取

DINOv1、v2

SLAM

point-based orb-slam line-based

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