Reactivity is a key component for autonomous vehicles navigating on natural terrains
in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess
traversability by processing on-board sensor data. This paper describes the case study of mobile
robot Andabata that classifies traversable points from 3D laser scans acquired in motion of its vicinity
to build 2D local traversability maps. Realistic robotic simulations with Gazebo were employed to
appropriately adjust reactive behaviors. As a result, successful navigation tests with Andabata using
the robot operating system (ROS) were performed on natural environments at low speeds.
