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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Research on BatSLAM Algorithm for UAV Based on Audio Perceptual Hash Closed-Loop Detection
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Research on BatSLAM Algorithm for UAV Based on Audio Perceptual Hash Closed-Loop Detection

机译：基于音频感知哈希闭环检测的无人机BatSLAM算法研究

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摘要

This research is aimed at the optimization of a two-dimensional (2D) empirical graph under a certain height and dark conditions for a UAV, using the bionic sonar system to replace the visual sensor's BatSLAM mode and audio perceptual hash closed-loop detection. The BatSLAM model uses Sum of Absolute Difference (SAD) image processing methods to update the bionic sonar template. This method only judges whether the appearance of the two cochlear images is consistent and does not have geometric processing and feature extraction. Because the cochlear images produce various noises during the acquisition and transmission, there are some differences in cochlear maps obtained at the same position, which can lead to the distortion of the constructed empirical map. In this research, an audio perceptual hash closed-loop detection algorithm is developed to extract features of cochlea. It considers both the appearance and the energy difference between adjacent bands to improve the accuracy of closed-loop detection, thus solving the distortion problem and improving the experience map. The simulation experiment shows that the improved BatSLAM model based on the audio perceptual hash closed-loop detection can improve the 2D experience map for UAV under certain height and dark conditions, through improving the accuracy of the closed-loop detection to solve the distortion problem and thus implementing the optimization of the experience graph.
机译：这项研究旨在利用仿生声纳系统替代视觉传感器的BatSLAM模式和音频感知哈希闭环检测，在无人机的特定高度和黑暗条件下优化二维（2D）经验图。 BatSLAM模型使用绝对差总和（SAD）图像处理方法来更新仿生声纳模板。该方法仅判断两个耳蜗图像的外观是否一致，并且没有进行几何处理和特征提取。由于耳蜗图像在采集和传输过程中会产生各种噪声，因此在相同位置获得的耳蜗图存在一些差异，这可能导致所构建的经验图失真。在这项研究中，开发了一种音频感知哈希闭环检测算法来提取耳蜗的特征。它同时考虑了相邻频段之间的外观和能量差，以提高闭环检测的准确性，从而解决了失真问题并改善了体验图。仿真实验表明，基于音频感知哈希闭环检测的改进的BatSLAM模型可以通过提高闭环检测的精度来解决变形问题，从而改善无人机在一定高度和黑暗条件下的二维体验图。从而实现了经验图的优化。

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