Outliers rejection for robust camera pose estimation using graduated non-convexity

Abstract

Camera pose estimation plays a crucial role in computer vision, which is widely used in augmented reality, robotics and autonomous driving. However, previous studies have neglected the presence of outliers in measurements, so that even a small percentage of outliers will significantly degrade precision. In order to deal with outliers, this paper proposes using a graduated non-convexity (GNC) method to suppress outliers in robust camera pose estimation, which serves as the core of GNCPnP. The authors first reformulate the camera pose estimation problem using a non-convex cost, which is less affected by outliers. Then, to apply a non-minimum solver to solve the reformulated problem, the authors use the Black-Rangarajan duality theory to transform it. Finally, to address the dependence of non-convex optimisation on initial values, the GNC method was customised according to the truncated least squares cost. The results of simulation and real experiments show that GNCPnP can effectively handle the interference of outliers and achieve higher accuracy compared to existing state-of-the-art algorithms. In particular, the camera pose estimation accuracy of GNCPnP in the case of a low percentage of outliers is almost comparable to that of the state-of-the-art algorithm in the case of no outliers.