上海科技大学知识管理系统

Rotation estimation is important for localization, mapping and navigation in robotic applications. A novel rotation estimation method for geometric vision of omni-directional cameras is proposed in this paper. Firstly, we formulate the rotation estimation as a sinusoid fitting problem on the basis of column-wise and row-wise shifts in omni-directional images. Then the method is implemented in two steps: motion vector extraction and sinusoid fitting. We are extracting motion vectors by finding pixel deviations in row- and column-directions as well as image rotation for each sub-image by Fourier-Mellin transform (FMT) or by optical flow. Then we fit these column-wise motion vectors to two sinusoid functions. Due to sharing the optimization variables, column-wise shifts and image rotation of each motion vector can be optimized jointly. Afterwards, the 3D rotation between two frames is obtained from the offsets, amplitudes and phase-shifts of the two sinusoid functions. Finally, we perform experiments for 3D rotation, which show that our algorithm outperforms the geometry-based methods in accuracy, robustness and speed. Compared to our early version, this paper makes several improvements: 1) modeling both pixel shifts and sub-regions' rotation to the sinusoid fitting problem, instead of only pixel shifts; 2) exploiting optical flow to extract motion vectors in addition to FMT; 3) comparing with a geometry-based method for pure rotation as well as five-point algorithms.