Abstract This paper examines the feasibility of using an audio-visual methodology for sound source localization of acoustic sources hidden from direct view. A four channel microphone array is used in conjunction with LiDAR and 2D/3D mapping to merge estimated angles of arrival with room parameters for sound source localization. The time difference of arrival (TDOA) approach is used to estimate the angle(s) of arrival from an unknown sound source and a reverse ray-tracing approach is used to identify a possible source location behind a barrier/obstacle. Using a single-source zone approach based TDOA algorithm, a variety of estimated source directions are identified, each arising from multiple sound sources due to reflections in the room. Results are combined with a three-dimensional map of the space acquired from a ground robot equipped with a 2D SICK LMS LiDAR, and a reverse ray tracing approach is used to triangulate the likely position of the source. Tests were performed in both controlled and uncontrolled environments and the method was capable of finding the hidden source to within 0.5 m accuracy, which is approximately 10% of the length of an automobile. It is proposed that a methodology like this could be used to add sound source localization capabilities on autonomous vehicles to detect the position of emergency/warning sounds in traffic that may be shielded from the direct field of view.

中文翻译：

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基于视听的非视距声源定位：可行性研究

摘要 本文研究了使用视听方法对无法直视的声源进行声源定位的可行性。四通道麦克风阵列与 LiDAR 和 2D/3D 映射结合使用，以将估计的到达角度与用于声源定位的房间参数合并。到达时间差 (TDOA) 方法用于估计未知声源的到达角度，反向光线追踪方法用于识别障碍物/障碍物后面的可能源位置。使用基于 TDOA 算法的单源区域方法，可以识别各种估计的源方向，每个方向都来自由于房间中的反射而产生的多个声源。结果与从配备 2D SICK LMS LiDAR 的地面机器人获取的空间三维地图相结合，并使用反向光线追踪方法对源的可能位置进行三角测量。在受控和非受控环境中进行了测试，该方法能够以 0.5 m 的精度找到隐藏源，大约是汽车长度的 10%。有人提议，可以使用这样的方法为自动驾驶汽车添加声源定位功能，以检测交通中可能被直接视场屏蔽的紧急/警告声音的位置。在受控和非受控环境中进行了测试，该方法能够以 0.5 m 的精度找到隐藏源，大约是汽车长度的 10%。有人提议，可以使用这样的方法为自动驾驶汽车添加声源定位功能，以检测交通中可能被直接视场屏蔽的紧急/警告声音的位置。在受控和非受控环境中进行了测试，该方法能够以 0.5 m 的精度找到隐藏源，大约是汽车长度的 10%。有人提议，可以使用这样的方法为自动驾驶汽车添加声源定位功能，以检测交通中可能被直接视场屏蔽的紧急/警告声音的位置。