  Recently, the emerging bio-inspired event cameras have demonstrated
potentials for a wide range of robotic applications in dynamic environments. In
this paper, we propose a novel fast and asynchronous event-based corner
detection method which is called FA-Harris. FA-Harris consists of several
components, including an event filter, a Global Surface of Active Events
(G-SAE) maintaining unit, a corner candidate selecting unit, and a corner
candidate refining unit. The proposed G-SAE maintenance algorithm and corner
candidate selection algorithm greatly enhance the real-time performance for
corner detection, while the corner candidate refinement algorithm maintains the
accuracy of performance by using an improved event-based Harris detector.
Additionally, FA-Harris does not require artificially synthesized event-frames
and can operate on asynchronous events directly. We implement the proposed
method in C++ and evaluate it on public Event Camera Datasets. The results show
that our method achieves approximately 8x speed-up when compared with
previously reported event-based Harris detector, and with no compromise on the
accuracy of performance.
