Edge-Based Meta-ICP Algorithm for Reliable Camera Pose Estimation

Camera pose estimation is crucial for 3D surface reconstruction and augmented reality applications. For systems equipped with RGB-D sensors, the corresponding transformation between frames can be effectively estimated using the iterative closest point (ICP) algorithms. Edge points, which cover most of the geometric structures in a frame, are good candidates for control points in ICP. However, the depth of object contour points is hard to accurately measure using commercial RGB-D sensors. Inspired by the model-agnostic meta-learning (MAML) algorithm, this work proposes a meta-ICP algorithm to jointly estimate the optimal transformation for multiple tasks, which are constructed by sampled datapoints. To increase task sampling efficiency, an edge-based task set partition algorithm is introduced for constructing complementary task sets. Moreover, to prevent ICP from being trapped in local minima, a dynamic model adaptation scheme is adopted to disturb the trapped tasks. Experimental results reveal that the probability of unstable estimations can be effectively reduced, indicating a much narrower error distribution of repeated experiments when adopting re-sampled points. With the proposed scheme, the overall absolute trajectory error can be improved by more than 30% as compared to the related edge-based methods using frame-to-frame pose estimation.