Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches. To deal with dynamic environments, computer vision researchers usually apply some learning-based object detectors to remove these dynamic objects. However, these object detectors are computationally too expensive for mobile robot on-board processing. In practical applications, these objects output noisy sounds that can be effectively detected by on-board sound source localization. The directional information of the sound source object can be efficiently obtained by direction of sound arrival (DoA) estimation, but depth estimation is difficult. Therefore, in this paper, we propose a novel audio-visual fusion approach that fuses sound source direction into the RGB-D image and thus removes the effect of dynamic obstacles on the multi-robot SLAM system. Experimental results of multi-robot SLAM in different dynamic environments show that the proposed method uses very small computational resources to obtain very stable self-localization results.

中文翻译：

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AcousticFusion：在动态环境中将声源定位与视觉 SLAM 融合

环境中的动态对象，例如人和其他代理，给现有的同步定位和映射 (SLAM) 方法带来了挑战。为了处理动态环境，计算机视觉研究人员通常会应用一些基于学习的对象检测器来移除这些动态对象。然而，这些物体检测器在计算上对于移动机器人车载处理来说太昂贵了。在实际应用中，这些物体输出嘈杂的声音，可以通过机载声源定位有效检测到。通过声音到达方向（DoA）估计可以有效地获得声源对象的方向信息，但深度估计比较困难。因此，在本文中，我们提出了一种新的视听融合方法，将声源方向融合到 RGB-D 图像中，从而消除动态障碍物对多机器人 SLAM 系统的影响。多机器人SLAM在不同动态环境下的实验结果表明，所提出的方法使用非常小的计算资源来获得非常稳定的自定位结果。