  In this article, we propose a novel LiDAR and event camera fusion modality
for subterranean (SubT) environments for fast and precise object and human
detection in a wide variety of adverse lighting conditions, such as low or no
light, high-contrast zones and in the presence of blinding light sources. In
the proposed approach, information from the event camera and LiDAR are fused to
localize a human or an object-of-interest in a robot's local frame. The local
detection is then transformed into the inertial frame and used to set
references for a Nonlinear Model Predictive Controller (NMPC) for reactive
tracking of humans or objects in SubT environments. The proposed novel fusion
uses intensity filtering and K-means clustering on the LiDAR point cloud and
frequency filtering and connectivity clustering on the events induced in an
event camera by the returning LiDAR beams. The centroids of the clusters in the
event camera and LiDAR streams are then paired to localize reflective markers
present on safety vests and signs in SubT environments. The efficacy of the
proposed scheme has been experimentally validated in a real SubT environment (a
mine) with a Pioneer 3AT mobile robot. The experimental results show real-time
performance for human detection and the NMPC-based controller allows for
reactive tracking of a human or object of interest, even in complete darkness.
