@inproceedings{nakath2018multi,
	author={Nakath, David and Clemens, Joachim and Schill, Kerstin},
	title = {Multi-Sensor Fusion and Active Perception for Autonomous Deep Space Navigation},
	booktitle={21st International Conference on Information Fusion (FUSION)},
	year={2018},
	month=jul,
  pages={2596-2605},
	publisher={IEEE},
	url={https://ieeexplore.ieee.org/document/8455788},
	doi={10.23919/ICIF.2018.8455788},
	abstract={Keeping track of the current state is a crucial task
	for mobile autonomous systems, which is referred to as state
	estimation. To solve that task, information from all available
	sensors needs to be fused, which includes relative measurements
	as well as observations of the surroundings. In a dynamic 3D
	environment, the pose of an agent has to be chosen such that
	the most relevant information can be observed. We propose an
	approach for multi-sensor fusion and active perception within
	an autonomous deep space navigation scenario. The probabilistic
	modeling of observables and sensors for that particular domain is
	described. For state estimation, we present an Extended Kalman
	Filter, an Unscented Kalman Filter, and a Particle Filter, which all
	operate on a manifold state space. Additionally, an approach for
	active perception is proposed, which selects the desired attitude
	of the spacecraft based on the knowledge about the dynamics
	of celestial objects, the kind of information they provide as
	well as the current uncertainty of the filters. We evaluated the
	localization performance of the algorithms within a simulation
	environment. The filters are compared to each other and we
	show that our active perception strategy outperforms two other
	information intake approaches.}
}