PoleStack: Robust Pole Estimation of Irregular Objects from Silhouette Stacking
This provides a robust method for pole estimation in space missions, such as during approach or hovering phases, but it is incremental as it builds on existing silhouette-based techniques.
The paper tackles the problem of estimating the rotation pole of irregular objects from silhouette images by stacking silhouettes to exploit reflective symmetry and using Fourier transforms for robustness, achieving degree-level accuracy with low-resolution images and robustness to shadowing and registration errors.
We present an algorithm to estimate the rotation pole of a principal-axis rotator using silhouette images collected from multiple camera poses. First, a set of images is stacked to form a single silhouette-stack image, where the object's rotation introduces reflective symmetry about the imaged pole direction. We estimate this projected-pole direction by identifying maximum symmetry in the silhouette stack. To handle unknown center-of-mass image location, we apply the Discrete Fourier Transform to produce the silhouette-stack amplitude spectrum, achieving translation invariance and increased robustness to noise. Second, the 3D pole orientation is estimated by combining two or more projected-pole measurements collected from different camera orientations. We demonstrate degree-level pole estimation accuracy using low-resolution imagery, showing robustness to severe surface shadowing and centroid-based image-registration errors. The proposed approach could be suitable for pole estimation during both the approach phase toward a target object and while hovering.