  A new robust and accurate approach for the detection and localization of
flying objects with the purpose of highly dynamic aerial interception and agile
multi-robot interaction is presented in this paper. The approach is proposed
for use onboard an autonomous aerial vehicle equipped with a 3D LiDAR sensor
providing input data for the algorithm. It relies on a novel 3D occupancy voxel
mapping method for the target detection and a cluster-based multiple hypothesis
tracker to compensate uncertainty of the sensory data. When compared to
state-of-the-art methods of onboard detection of other flying objects, the
presented approach provides superior localization accuracy and robustness to
different environments and appearance changes of the target, as well as a
greater detection range. Furthermore, in combination with the proposed
multi-target tracker, sporadic false positives are suppressed, state estimation
of the target is provided and the detection latency is negligible. This makes
the detector suitable for tasks of agile multi-robot interaction, such as
autonomous aerial interception or formation control where precise, robust, and
fast relative localization of other robots is crucial. We demonstrate the
practical usability and performance of the system in simulated and real-world
experiments.
