  Currently, mobile robots are developing rapidly and are finding numerous
applications in the industry. However, several problems remain related to their
practical use, such as the need for expensive hardware and high power
consumption levels. In this study, we build a low-cost indoor mobile robot
platform that does not include a LiDAR or a GPU. Then, we design an autonomous
navigation architecture that guarantees real-time performance on our platform
with an RGB-D camera and a low-end off-the-shelf single board computer. The
overall system includes SLAM, global path planning, ground segmentation, and
motion planning. The proposed ground segmentation approach extracts a
traversability map from raw depth images for the safe driving of low-body
mobile robots. We apply both rule-based and learning-based navigation policies
using the traversability map. Running sensor data processing and other
autonomous driving components simultaneously, our navigation policies perform
rapidly at a refresh rate of 18 Hz for control command, whereas other systems
have slower refresh rates. Our methods show better performances than current
state-of-the-art navigation approaches within limited computation resources as
shown in 3D simulation tests. In addition, we demonstrate the applicability of
our mobile robot system through successful autonomous driving in an indoor
environment.
