Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who are familiar with visual localization techniques. This paper explores the problem of automatic marker placement within a scene. Specifically, given a predetermined set of markers and a scene model, we compute optimized marker positions within the scene that can improve accuracy in visual localization. Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene. We present optimized marker placement (OMP), a greedy algorithm that is based on the camera localizability framework. We have also designed a simulation framework for testing marker placement algorithms on 3D models and images generated from synthetic scenes. We have evaluated OMP within this testbed and demonstrate an improvement in the localization rate by up to 20 percent on four different scenes.

翻译：在场景中添加基准标记是增强视觉定位算法鲁棒性的常用策略。传统上，这些标记位置由熟悉视觉定位技术的人类专家手动选择。本文探索了场景中自动标记放置问题，具体而言，在给定预定义标记集和场景模型的情况下，我们计算场景中优化后的标记位置，以提升视觉定位精度。我们的主要贡献在于提出了一种新颖的相机可定位性建模框架，该框架同时整合了自然场景特征与人工添加的基准标记。我们提出了基于该框架的贪婪算法——优化标记放置（OMP）。同时，我们设计了一套仿真测试平台，用于在合成场景的三维模型及生成图像上评估标记放置算法。在该测试平台上对OMP进行评测后，实验结果表明在四种不同场景中，定位成功率最高可提升20%。