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Abstract：
With the rapid development of modern science and technology, navigation technology provides great convenience for people\'s life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.
摘要：
随着现代科学技术的飞速发展,导航技术为人们的生活提供了极大的便利,但是在复杂环境中定位不准确的问题一直是导航技术亟待解决的挑战。为了应对这一挑战，本文提出了一种结合图像分割和多传感器融合跟踪配准的增强现实导航方法。该方法通过GA-OTSU-Canny算法优化图像处理过程,结合高精度多传感器信息,实现复杂环境下定位制导的精确跟踪。实验结果表明,GA-OTSU-Canny算法具有较快的图像边缘分割率,最快的启动速度仅为1.8s，该导航系统结合了图像分割和传感器跟踪和配准技术，在现实导航中具有高效的性能，其建筑物识别率均在99%以上。增强现实导航系统不仅提高了高层和城市峡谷环境中的导航精度,而且在导航启动时间和目标建筑物识别精度方面也明显优于传统导航解决方案。总之,本研究不仅为图像处理和多传感器数据的理论集成提供了一个新的框架,而且还为实际导航系统的开发和应用带来了创新的技术解决方案。
