  Event cameras are a kind of bio-inspired sensors that generate data when the
brightness changes. Because of the advantages of low-latency and high dynamic
range (HDR), they are widely used in the field of mobile robots. However, due
to the nature of the sparse event stream, event-based mapping can only obtain
sparse or semi-dense edge 3D maps. By contrast, standard cameras provide
complete frames. To leverage the complementarity of event-based and standard
frame-based cameras, we propose a fusion strategy for dense mapping in this
paper. We first generate an edge map from events, and then fill the map using
frames to obtain the dense depth map.
