  Audio-visual navigation combines sight and hearing to navigate to a
sound-emitting source in an unmapped environment. While recent approaches have
demonstrated the benefits of audio input to detect and find the goal, they
focus on clean and static sound sources and struggle to generalize to unheard
sounds. In this work, we propose the novel dynamic audio-visual navigation
benchmark which requires catching a moving sound source in an environment with
noisy and distracting sounds, posing a range of new challenges. We introduce a
reinforcement learning approach that learns a robust navigation policy for
these complex settings. To achieve this, we propose an architecture that fuses
audio-visual information in the spatial feature space to learn correlations of
geometric information inherent in both local maps and audio signals. We
demonstrate that our approach consistently outperforms the current
state-of-the-art by a large margin across all tasks of moving sounds, unheard
sounds, and noisy environments, on two challenging 3D scanned real-world
environments, namely Matterport3D and Replica. The benchmark is available at
http://dav-nav.cs.uni-freiburg.de.
