OctoSLAM: A 3D mapping approach to situational awareness of unmanned aerial vehicles

The focus of this paper is on situational awareness of airborne agents capable of 6D motion, in particular multi-rotor UAVs. We propose the fusion of 2D laser range finder, altitude, and attitude sensor data in order to perform simultaneous localization and mapping (SLAM) indoors. In contrast to other planar 2D laser range finder based SLAM approaches, we perform SLAM on a 3D instead of a 2D map. To represent the 3D environment an octree based map is used. Our scan registration algorithm is derived from Hector SLAM. We evaluate the performance of our system in simulation and on a real multirotor UAV equipped with a 2D laser range finder, inertial measurement unit, and altitude sensor. The results show significant improvement in the localization and representation accuracy over current 2D map SLAM methods. The system is implemented using Willow Garage's robot operating system.