  This paper presents a novel obstacle avoidance system for road robots
equipped with RGB-D sensor that captures scenes of its way forward. The purpose
of the system is to have road robots move around autonomously and constantly
without any collision even with small obstacles, which are often missed by
existing solutions. For each input RGB-D image, the system uses a new two-stage
semantic segmentation network followed by the morphological processing to
generate the accurate semantic map containing road and obstacles. Based on the
map, the local path planning is applied to avoid possible collision.
Additionally, optical flow supervision and motion blurring augmented training
scheme is applied to improve temporal consistency between adjacent frames and
overcome the disturbance caused by camera shake. Various experiments are
conducted to show that the proposed architecture obtains high performance both
in indoor and outdoor scenarios.
