DUNE: Sim2Real Transfer for Depth-based Navigation in Unstructured Dynamic Indoor Environments

Collision-free navigation in dynamic environments, especially with moving pedestrians, is crucial for mobile robots. This paper introduces DUNE, a depth-based policy trained in simulation for collision-free navigation of Ackermann mobile robots in unstructured indoor environments. DUNE uses a CNN-LSTM to encode depth vision and past actions, while an actor-critic network controls velocity and steering. Additionally, a depth filter bridges the sim-to-real gap. Unlike prior works relying on LIDAR and complex algorithms, DUNE achieves state-of-the-art performance with only egocentric depth perception and lightweight neural networks, both in simulation and real-world tasks.