Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications
<|reference_start|>Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications: Unlike a traditional gyroscope, a visual gyroscope estimates camera rotation through images. The integration of omnidirectional cameras, offering a larger field of view compared to traditional RGB cameras, has proven to yield more accurate and robust results. However, challenges arise in situations that lack features, have substantial noise causing significant errors, and where certain features in the images lack sufficient strength, leading to less precise prediction results.   Here, we address these challenges by introducing a novel visual gyroscope, which combines an Efficient Multi-Mask-Filter Rotation Estimator(EMMFRE) and a Learning based optimization(LbTO) to provide a more efficient and accurate rotation estimation from spherical images. Experimental results demonstrate superior performance of the proposed approach in terms of accuracy. The paper emphasizes the advantages of integrating machine learning to optimize analytical solutions, discusses limitations, and suggests directions for future research.<|reference_end|>
@article{du2024toward,
    title={Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics
  Filtering, and Masking Techniques for Spherical Camera Applications},
    author={Yao Du, Carlos M. Mateo, Mirjana Maras, Tsun-Hsuan Wang, Marc
  Blanchon, Alexander Amini, Daniela Rus, Omar Tahri},
    journal={arXiv preprint arXiv:2404.01924},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01924},
    primaryClass={cs.CV}
}