Towards a Unified Approach to Homography Estimation Using Image Features and Pixel Intensities
This work addresses a key challenge in vision-based robotics by enhancing homography estimation, though it is incremental as it builds on existing sequential combination techniques.
The paper tackled the problem of homography estimation by proposing a unified hybrid method that combines feature- and intensity-based approaches into a single nonlinear optimization, resulting in improved convergence properties compared to individual methods, as validated in experiments and a visual tracking application.
The homography matrix is a key component in various vision-based robotic tasks. Traditionally, homography estimation algorithms are classified into feature- or intensity-based. The main advantages of the latter are their versatility, accuracy, and robustness to arbitrary illumination changes. On the other hand, they have a smaller domain of convergence than the feature-based solutions. Their combination is hence promising, but existing techniques only apply them sequentially. This paper proposes a new hybrid method that unifies both classes into a single nonlinear optimization procedure, applies the same minimization method, and uses the same homography parametrization and warping function. Experimental validation using a classical testing framework shows that the proposed unified approach has improved convergence properties compared to each individual class. These are also demonstrated in a visual tracking application. As a final contribution, our ready-to-use implementation of the algorithm is made publicly available to the research community.