  Event-based cameras are vision devices that transmit only brightness changes
with low latency and ultra-low power consumption. Such characteristics make
event-based cameras attractive in the field of localization and object tracking
in resource-constrained systems. Since the number of generated events in such
cameras is huge, the selection and filtering of the incoming events are
beneficial from both increasing the accuracy of the features and reducing the
computational load. In this paper, we present an algorithm to detect
asynchronous corners from a stream of events in real-time on embedded systems.
The algorithm is called the Three Layer Filtering-Harris or TLF-Harris
algorithm. The algorithm is based on an events' filtering strategy whose
purpose is 1) to increase the accuracy by deliberately eliminating some
incoming events, i.e., noise, and 2) to improve the real-time performance of
the system, i.e., preserving a constant throughput in terms of input events per
second, by discarding unnecessary events with a limited accuracy loss. An
approximation of the Harris algorithm, in turn, is used to exploit its
high-quality detection capability with a low-complexity implementation to
enable seamless real-time performance on embedded computing platforms. The
proposed algorithm is capable of selecting the best corner candidate among
neighbors and achieves an average execution time savings of 59 % compared with
the conventional Harris score. Moreover, our approach outperforms the competing
methods, such as eFAST, eHarris, and FA-Harris, in terms of real-time
performance, and surpasses Arc* in terms of accuracy.
