  Robots are increasingly operating in indoor environments designed for and
shared with people. However, robots working safely and autonomously in uneven
and unstructured environments still face great challenges. Many modern indoor
environments are designed with wheelchair accessibility in mind. This presents
an opportunity for wheeled robots to navigate through sloped areas while
avoiding staircases. In this paper, we present an integrated software and
hardware system for autonomous mobile robot navigation in uneven and
unstructured indoor environments. This modular and reusable software framework
incorporates capabilities of perception and navigation. Our robot first builds
a 3D OctoMap representation for the uneven environment with the 3D mapping
using wheel odometry, 2D laser and RGB-D data. Then we project multilayer 2D
occupancy maps from OctoMap to generate the the traversable map based on layer
differences. The safe traversable map serves as the input for efficient
autonomous navigation. Furthermore, we employ a variable step size Rapidly
Exploring Random Trees that could adjust the step size automatically,
eliminating tuning step sizes according to environments. We conduct extensive
experiments in simulation and real-world, demonstrating the efficacy and
efficiency of our system.
