  Robot navigation in mapless environment is one of the essential problems and
challenges in mobile robots. Deep reinforcement learning is a promising
technique to tackle the task of mapless navigation. Since reinforcement
learning requires a lot of explorations, it is usually necessary to train the
agent in the simulator and then migrate to the real environment. The big
reality gap makes RGB image, the most common visual sensor, rarely used. In
this paper we present a learning-based mapless motion planner by taking RGB
images as visual inputs. Many parameters in end-to-end navigation network
taking RGB images as visual input are used to extract visual features.
Therefore, we decouple visual features extracted module from the reinforcement
learning network to reduce the need of interactions between agent and
environment. We use Variational Autoencoder (VAE) to encode the image, and
input the obtained latent vector as low-dimensional visual features into the
network together with the target and motion information, so that the sampling
efficiency of the agent is greatly improved. We built simulation environment as
robot navigation environment for algorithm comparison. In the test environment,
the proposed method was compared with the end-to-end network, which proved its
effectiveness and efficiency. The source code is available:
https://github.com/marooncn/navbot.
