Robust 6DoF Pose Tracking Considering Contour and Interior Correspondence Uncertainty for AR Assembly Guidance
Journal:
arXiv
Published Date:
Feb 17, 2025
Abstract
Augmented reality assembly guidance is essential for intelligent
manufacturing and medical applications, requiring continuous measurement of the
6DoF poses of manipulated objects. Although current tracking methods have made
significant advancements in accuracy and efficiency, they still face challenges
in robustness when dealing with cluttered backgrounds, rotationally symmetric
objects, and noisy sequences. In this paper, we first propose a robust
contour-based pose tracking method that addresses error-prone contour
correspondences and improves noise tolerance. It utilizes a fan-shaped search
strategy to refine correspondences and models local contour shape and noise
uncertainty as mixed probability distribution, resulting in a highly robust
contour energy function. Secondly, we introduce a CPU-only strategy to better
track rotationally symmetric objects and assist the contour-based method in
overcoming local minima by exploring sparse interior correspondences. This is
achieved by pre-sampling interior points from sparse viewpoint templates
offline and using the DIS optical flow algorithm to compute their
correspondences during tracking. Finally, we formulate a unified energy
function to fuse contour and interior information, which is solvable using a
re-weighted least squares algorithm. Experiments on public datasets and real
scenarios demonstrate that our method significantly outperforms
state-of-the-art monocular tracking methods and can achieve more than 100 FPS
using only a CPU.
