自识别标记(self-identifying marker) -(1) 简介

本文介绍了自识别标记(self-identifying marker)的概念及其在相机标定、机器人导航和增强现实中的应用。自识别标记阵列图案能自动建立三维空间与二维图像间的对应关系,提高相机标定效率。在增强现实中,它们用于互动游戏和创意广告,提供了一种有效的识别方式。

一、什么是自识别标记(Self-identifying marker)?

自识别标记在不同的论文中有不同称谓,比如self-identifying marker, self-identifying marker pattern, fiducial marker等, 在此我们统称为自识别标记。 自识别标记乍一看有点类似我们常见的二维码,其每个标记具有唯一性。和二维码不同的是,自识别标记在实际应用中通常由多个一起组合成规则的标记阵列图案(如下图1为8 x 4阵列)。
这里写图片描述
图1 自识别标记阵列图案示例

二、自识别标记有哪些应用?

自识别标记主要有如下应用:相机标定、安防监控、机器人视觉导航、电影拍摄中的特效制作,以及最近风头正盛的增强现实等。下面举例说明。

1、相机标定

自识别标记图案的一个重要应用就是相机标定。相机标定方法有:传统相机标定法、主动视觉相机标定方法、相机自标定法。在此我们主要关注传统相机标定法。
传统相机标定法需要使用尺寸已知的标定物,通过建立标定物上三维空间坐标已知的点与其在一幅图像或多幅图像上二维投影点之间的对应关系,利用一定的算法获得相机模型的内外参数。根据标定物的不同可分为三维标定物和平面标定物(比如棋盘格)。三维标定物可由单幅图像进行标定,标定精度较高,但高精密三维标定物的加工和维护较困难。平面标定物比三维标定物制作简单,一般可以打印张贴在平面上,方便易用,精度也可以保证,但必须采用多幅不同角度拍摄的图像才能完成标定。
根据对应关系来计算相机模型的内外参的算法已经研究的比较深入,比较有名的有张正友标定法,加州理工标定法等。那么问题来了,这个对应关系怎么找?实际上关于确定对应关系的方法研究的人比较少。这是因为,传统的标定方法是个离线的、一劳永逸的过程,一个人可以依靠人工的方法去确定对应关系,通俗点说

### Self-Planning in Computer Science Self-planning refers to systems capable of generating plans autonomously without human intervention. In computer science and software development, self-planning involves algorithms that can dynamically adapt their behavior based on changing conditions or goals[^1]. These systems often incorporate artificial intelligence techniques such as machine learning, decision theory, and optimization methods. A typical implementation approach includes defining objectives, identifying constraints, evaluating available resources, and selecting appropriate strategies. For instance, an autonomous vehicle might use sensor data combined with predefined rules to navigate safely while avoiding obstacles. In terms of practical applications, self-planning technologies are widely used across various domains including robotics, logistics management, manufacturing processes, game playing agents, etc. One notable example comes from the University of Maryland Autonomous Vehicle Laboratory which focuses specifically on biologically-inspired designs for robotic platforms. For educational purposes, institutions like those mentioned offer advanced courses covering relevant topics within master's programs aimed at preparing students for careers involving complex problem solving skills required when developing intelligent systems able to perform self-planning tasks effectively[^2]. ```python def plan_route(current_location, destination): # Define planning logic here using AI/ML models trained previously. route_options = get_possible_routes() best_option = evaluate_and_select_best(route_options) return best_option # Example usage optimal_path = plan_route('start_point', 'end_point') print(f"The recommended path is {optimal_path}.") ```
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