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.
Event cameras are a kind of bio-inspired sensors that generate data when the brightness changes , which are of low-latency and high dynamic range (HDR) . 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. We propose "filling score" to evaluate the quality of filled results and show that our strategy can increase the number of existing semi-dense 3D map.
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