  In this paper, we propose a map-based end-to-end DRL approach for
three-dimensional (3D) obstacle avoidance in a partially observed environment,
which is applied to achieve autonomous navigation for an indoor mobile robot
using a depth camera with a narrow field of view. We first train a neural
network with LSTM units in a 3D simulator of mobile robots to approximate the
Q-value function in double DRQN. We also use a curriculum learning strategy to
accelerate and stabilize the training process. Then we deploy the trained model
to a real robot to perform 3D obstacle avoidance in its navigation. We evaluate
the proposed approach both in the simulated environment and on a robot in the
real world. The experimental results show that the approach is efficient and
easy to be deployed, and it performs well for 3D obstacle avoidance with a
narrow observation angle, which outperforms other existing DRL-based models by
15.5% on success rate.
