Finding Next Best Views for Autonomous UAV Mapping through GPU-Accelerated Particle Simulation
Institute of Technical Aspects of Multimodal Systems, Department of Computer Science, University of Hamburg, Germany
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
@InProceedings{adler2013iros,
author={Junhao Xiao and Benjamin Adler and Houxiang Zhang},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},
title={Finding Next Best Views for Autonomous UAV Mapping through GPU-Accelerated Particle Simulation},
year={2013},
month={nov.}
}
This paper presents a novel algorithm capable of generating multiple next best views (NBVs), sorted by achievable information gain. Although being designed for waypoint 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/.
August 10, 2013 by hgpu
