Research on BatSLAM Algorithm for UAV Based on Audio Perceptual Hash Closed-Loop Detection

Abstract

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.