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文章目录

• • • 点云配准 ICP 算法
    • AprilTags Visual Fiducial System
    • 结构化场景
  • 二级标题
  • 待补充
  • • 待补充

点云配准 ICP 算法

  This tutorial demonstrates the ICP (Iterative Closest Point) registration algorithm. It has been a mainstay of geometric registration in both research and industry for many years. The input are two point clouds and an initial transformation that roughly aligns the source point cloud to the target point cloud. The output is a refined transformation that tightly aligns the two point clouds. A helper function draw_registration_result visualizes the alignment during the registration process. In this tutorial, we show two ICP variants, the point-to-point ICP and the point-to-plane ICP [Rusinkiewicz2001]_.
ICP Registration — Open3D latest (664eff5) documentation

AprilTags Visual Fiducial System

  AprilTag is a visual fiducial system, useful for a wide variety of tasks including augmented reality, robotics, and camera calibration. Targets can be created from an ordinary printer, and the AprilTag detection software computes the precise 3D position, orientation, and identity of the tags relative to the camera. The AprilTag library is implemented in C with no external dependencies. It is designed to be easily included in other applications, as well as be portable to embedded devices. Real-time performance can be achieved even on cell-phone grade processors.
AprilTag
GitHub - AprilRobotics/apriltag: AprilTag is a visual fiducial system popular for robotics research.
AprilTag with Python - PyImageSearch 20201102
Apriltag原理简介及源代码_Rain的博客-CSDN博客_apriltag 20190516
GitHub - BlackJocker1995/Apriltag_python: restruct the apriltag using python

结构化场景

structured senario, structured scene, structured environments;
模式识别领域的结构化场景和非结构化场景是如何定义的？ - 知乎
结构化数据、半结构化数据和非结构化数据有什么区别以及应用场景有哪些？_小慌慌的博客 20200906
结构化、半结构化和非结构化数据 - 云+社区 - 腾讯云 20181008

二级标题

待补充

待补充