A Robust Roll Angle Estimation Algorithm Based on Gradient Descent
This is an incremental improvement for applications requiring efficient roll angle estimation, such as in robotics or computer vision.
The paper tackles the problem of roll angle estimation by proposing a gradient descent optimization method to improve computational efficiency over a previous golden section search approach, achieving the same precision with fewer iterations.
This paper presents a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense disparity map by minimizing a global energy using golden section search algorithm. In this paper, to achieve greater computational efficiency, we utilize gradient descent to optimize the aforementioned global energy. The experimental results illustrate that the proposed roll angle estimation algorithm takes fewer iterations to achieve the same precision as the previous method.