A temporal attention-guided fusion module plus attention-modulated loss raises multimodal SNN accuracy to 77.55% on CREMA-D, 70.65% on AVE, and 97.5% on EAD.
High-precision visual navigation device calibration method based on collimator
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abstract
Visual navigation devices require precise calibration to achieve high-precision localization and navigation, which includes camera and attitude calibration. To address the limitations of time-consuming camera calibration and complex attitude adjustment processes, this study presents a collimator-based calibration method and system. Based on the optical characteristics of the collimator, a single-image camera calibration algorithm is introduced. In addition, integrated with the precision adjustment mechanism of the calibration frame, a rotation transfer model between coordinate systems enables efficient attitude calibration. Experimental results demonstrate that the proposed method achieves accuracy and stability comparable to traditional multi-image calibration techniques. Specifically, the re-projection errors are less than 0.1463 pixels, and average attitude angle errors are less than 0.0586 degrees with a standard deviation less than 0.0257 degrees, demonstrating high precision and robustness.
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Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning
A temporal attention-guided fusion module plus attention-modulated loss raises multimodal SNN accuracy to 77.55% on CREMA-D, 70.65% on AVE, and 97.5% on EAD.