This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
Youjie Zhou, Guofeng Mei, Yiming Wang, Yi Wan, Fabio Poiesi
Outline
In this paper, we propose an efficient multimodal fusion SLAM method, FMF-SLAM, to solve the visual simultaneous localization and mapping (SLAM) problem in challenging environments such as noise, changing illumination conditions, and dark environments. FMF-SLAM improves the algorithm efficiency by utilizing the fast Fourier transform (FFT) and introduces novel Fourier-based self-attention and cross-attention mechanisms to extract features from RGB and depth signals. In addition, it enhances the interaction of multimodal features by incorporating multi-scale knowledge distillation between multimodals. Through the fusion with GNSS-RTK and global bundle adjustment, we demonstrate the feasibility of FMF-SLAM in real-world scenarios with real-time performance by integrating it into a security robot. We demonstrate the state-of-the-art performance in noise, changing illumination, and dark conditions through video sequences using TUM, TartanAir, and real-world datasets. The code and datasets are available at https://github.com/youjie-zhou/FMF-SLAM.git .