Finding next best views for autonomous UAV mapping through GPU-accelerated particle simulation

This paper presents a novel algorithm capable of generating multiple next best views (NBVs), sorted by achievable information gain. Although being designed for way-point generation in autonomous airborne mapping of outdoor environments, it works directly on raw point clouds and thus can be used with any sensor generating spatial occupancy information (e.g. LIDAR, kinect or Time-of-Flight cameras). To satisfy time-constraints introduced by operation on UAVs, the algorithm is implemented on a highly parallel architecture and benchmarked against the previous, CPU-based proof of concept. As the underlying hardware imposes limitations with regards to memory access and concurrency, necessary data structures and further performance considerations are explained in detail. Open-source code for this paper is available at http://www.github.com/benadler/.