Bio-inspired Event-Based (EB) cameras are a promising new technology that outperforms standard frame-based cameras in extreme lighted and fast moving scenes. Already, a number of EB corner detection techniques have been developed; however, the performance of these EB corner detectors has only been evaluated based on a few author-selected criteria rather than on a unified common basis, as proposed here. Moreover, their experimental conditions are mainly limited to less interesting operational regions of the EB camera (on which frame-based cameras can also operate), and some of the criteria, by definition, could not distinguish if the detector had any systematic bias. In this paper, we evaluate five of the seven existing EB corner detectors on a public dataset including extreme illumination conditions that have not been investigated before. Moreover, this evaluation is the first of its kind in terms of analysing not only such a high number of detectors, but also applying a unified procedure for all. Contrary to previous assessments, we employed both the intensity and trajectory information within the public dataset rather than only one of them. We show that a rigorous comparison among EB detectors can be performed without tedious manual labelling and even with challenging acquisition conditions. This study thus proposes the first standard unified EB corner evaluation procedure, which will enable better understanding of the underlying mechanisms of EB cameras and can therefore lead to more efficient EB corner detection techniques.

In space, visual-based relative navigation systems suffer from dynamic illumination conditions of the target (eclipse conditions, solar glare, etc.) where most of these issues are addressed by advanced mission planning techniques. However, such planning would not be always feasible or even if it is, it would not be straightforward for active debris removal missions. On the other hand, using an infrared-based system would overcome this problem, if a guideline to predict infrared signature of space debris based on the target thermal profile could be provided for algorithm design and testing. Spacecraft thermal design is unique to every platform. This means every active debris removal target will have a different infrared signature, which changes over time not just only due to orbital dynamics but also due to its thermal surface coatings. In order to provide a space debris infrared signature guideline for most of the possible active debris removal targets, we introduce an innovative grouping system for thermal surface coatings based on their behaviour in space environment. Through the use of this grouping system, we propose a space debris infrared signature estimation method, which was extensively verified by our simulations and experiments. During our verifications, we have also discovered very important problem so-called ‘signature ambiguity’ that is unique to infrared-based active debris removal systems, which we have also discussed in our work.