  Globally consistent dense maps are a key requirement for long-term robot
navigation in complex environments. While previous works have addressed the
challenges of dense mapping and global consistency, most require more
computational resources than may be available on-board small robots. We propose
a framework that creates globally consistent volumetric maps on a CPU and is
lightweight enough to run on computationally constrained platforms. Our
approach represents the environment as a collection of overlapping Signed
Distance Function (SDF) submaps, and maintains global consistency by computing
an optimal alignment of the submap collection. By exploiting the underlying SDF
representation, we generate correspondence free constraints between submap
pairs that are computationally efficient enough to optimize the global problem
each time a new submap is added. We deploy the proposed system on a hexacopter
Micro Aerial Vehicle (MAV) with an Intel i7-8650U CPU in two realistic
scenarios: mapping a large-scale area using a 3D LiDAR, and mapping an
industrial space using an RGB-D camera. In the large-scale outdoor experiments,
the system optimizes a 120x80m map in less than 4s and produces absolute
trajectory RMSEs of less than 1m over 400m trajectories. Our complete system,
called voxgraph, is available as open source.
