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20世纪中期，随着camera和computer的出现， photogrammetry (摄影测量学)也开始研究adjustment computation，所以他们给起了个名字叫bundle adjustment (隶属摄影测量学科前辈的功劳)。 总结来说，Bundle Adjustment是一种强大的优化工具，通过联合优化相机参数和三维点坐标，最小化重投影误差，从而提升计算机视觉中三维重建与定位任务的精度。 为了 求解相机的位姿 (R,t) 和空间 三维点的3D坐标，常采用优化的方式，优化方法中常用的是 光束平差法 （BA，又叫捆绑调整，束调整等）。

光束法平差 (Bundle Adjustment)是摄影测量与SLAM中的核心优化算法，源于19世纪大地测量学。 文章详解BA的历史发展、最小二乘原理及四种实现代码，包括相机位姿、三维点优化及Ceres Solver应用，适合计算机视觉与测绘领域研究者参考。 It does so by minimizing the sum of projection errors. Bundle Adjustment即光束法平差，它通过将相机的姿态和测量点的三维坐标作为未知参数，将影像上探测到的用于前方交会的特征点坐标作为观测数据从而进行平差得到最优的相机参数和世界点坐标。

总结：束调整是通过最小化重投影误差，对相机参数和三维点坐标进行全局联合优化的核心技术。 它起源于大地测量学，已成为现代计算机视觉、遥感测绘和机器人定位不可或缺的数学工具。

BundleAdjustment（BA）,又称光束法平差,是SFM和SLAM算法中用于优化3D模型和相机参数的关键步骤。 它通过最小化重投影误差来精炼相机位姿和特征点空间位置，提升视觉重建的精度。 从每个特征点反射出来的几束光线（bundles of light rays），在我们把相机姿态和特征点的位置做出最优的调整（adjustment）之后，最后收束到光心的这个过程，简称BA。 光束法平差（bundle adjustment）旨在优化相机位姿和地标点，在SfM和SLAM中关键应用。 源自19世纪大地测量，经摄影测量发展，现多用于机器人SLAM。 In this article, we discussed bundle adjustment, a method that enhances camera settings and 3d structure estimates in visual applications

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