基于透视变形的多视点残差协方差模型
A Model for Multi-View Residual Covariances based on Perspective Deformation
摘要：
在这项工作中，我们推导出一个模型的协方差的视觉残差在多视图SfM，里程计和SLAM设置。我们的方法的核心是制定的残差协方差的几何和光度噪声源的组合。我们的关键新贡献是推导出一个术语，该术语建模了当对一个点周围的3D表面进行成像时，局部2D补丁如何遭受透视变形。总之，这些加起来一个有效的和一般的配方，不仅提高了基于特征的方法和直接的方法的准确性，但也可以用来估计更准确的测量的状态熵，从而更好地建立点的可见性阈值。我们使用合成和真实数据验证我们的模型，并将其集成到光度和基于特征的光束平差中，以可忽略不计的开销提高其精度。
英文摘要：
In this work, we derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. The core of our approach is the formulation of the residual covariances as a combination of geometric and photometric noise sources. And our key novel contribution is the derivation of a term modelling how local 2D patches suffer from perspective deformation when imaging 3D surfaces around a point. Together, these add up to an efficient and general formulation which not only improves the accuracy of both feature-based and direct methods, but can also be used to estimate more accurate measures of the state entropy and hence better founded point visibility thresholds. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment, improving their accuracy with a negligible overhead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
