  Autonomous navigation in complex environments is a crucial task in
time-sensitive scenarios such as disaster response or search and rescue.
However, complex environments pose significant challenges for autonomous
platforms to navigate due to their challenging properties: constrained narrow
passages, unstable pathway with debris and obstacles, or irregular geological
structures and poor lighting conditions. In this work, we propose a multimodal
fusion approach to address the problem of autonomous navigation in complex
environments such as collapsed cites, or natural caves. We first simulate the
complex environments in a physics-based simulation engine and collect a
large-scale dataset for training. We then propose a Navigation Multimodal
Fusion Network (NMFNet) which has three branches to effectively handle three
visual modalities: laser, RGB images, and point cloud data. The extensively
experimental results show that our NMFNet outperforms recent state of the art
by a fair margin while achieving real-time performance. We further show that
the use of multiple modalities is essential for autonomous navigation in
complex environments. Finally, we successfully deploy our network to both
simulated and real mobile robots.
