  In this paper, we present a new system for live collaborative dense surface
reconstruction. Cooperative robotics, multi participant augmented reality and
human-robot interaction are all examples of situations where collaborative
mapping can be leveraged for greater agent autonomy. Our system builds on
ElasticFusion to allow a number of cameras starting with unknown initial
relative positions to maintain local maps utilising the original algorithm.
Carrying out visual place recognition across these local maps the system can
identify when two maps overlap in space, providing an inter-map constraint from
which the system can derive the relative poses of the two maps. Using these
resulting pose constraints, our system performs map merging, allowing multiple
cameras to fuse their measurements into a single shared reconstruction. The
advantage of this approach is that it avoids replication of structures
subsequent to loop closures, where multiple cameras traverse the same regions
of the environment. Furthermore, it allows cameras to directly exploit and
update regions of the environment previously mapped by other cameras within the
system. We provide both quantitative and qualitative analyses using the
synthetic ICL-NUIM dataset and the real-world Freiburg dataset including the
impact of multi-camera mapping on surface reconstruction accuracy, camera pose
estimation accuracy and overall processing time. We also include qualitative
results in the form of sample reconstructions of room sized environments with
up to 3 cameras undergoing intersecting and loopy trajectories.
