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

Unmanned aerial vehicles (UAVs) have recently become widely available to the research community. A common vision is that such (semi-)autonomous airborne agents can be beneficial in numerous scenarios, e.g. urban search and rescue. However, when deploying computationally restricted UAVs in these real life scenarios, various challenges from multiple research domains arise. These include situational awareness, controlling, planning, and learning. The focus of this demonstration is on situational awareness of agents capable of 6D motion, in particular UAVs. We propose the integration of 2D laser range finder, altitude, and attitude sensor data to compose 3D maps of the environment. Experiments show significant improvement in the localization and representation accuracy over current 2D map methods.