  Deep reinforcement learning has achieved great success in laser-based
collision avoidance works because the laser can sense accurate depth
information without too much redundant data, which can maintain the robustness
of the algorithm when it is migrated from the simulation environment to the
real world. However, high-cost laser devices are not only difficult to deploy
for a large scale of robots but also demonstrate unsatisfactory robustness
towards the complex obstacles, including irregular obstacles, e.g., tables,
chairs, and shelves, as well as complex ground and special materials. In this
paper, we propose a novel monocular camera-based complex obstacle avoidance
framework. Particularly, we innovatively transform the captured RGB images to
pseudo-laser measurements for efficient deep reinforcement learning. Compared
to the traditional laser measurement captured at a certain height that only
contains one-dimensional distance information away from the neighboring
obstacles, our proposed pseudo-laser measurement fuses the depth and semantic
information of the captured RGB image, which makes our method effective for
complex obstacles. We also design a feature extraction guidance module to
weight the input pseudo-laser measurement, and the agent has more reasonable
attention for the current state, which is conducive to improving the accuracy
and efficiency of the obstacle avoidance policy.
