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Globally consistent dense maps are a key requirement for long-term robotnavigation in complex environments. While previous works have addressed thechallenges of dense mapping and global consistency, most require morecomputational resources than may be available on-board small robots. We proposea framework that creates globally consistent volumetric maps on a CPU and islightweight enough to run on computationally constrained platforms. Ourapproach represents the environment as a collection of overlapping SignedDistance Function (SDF) submaps, and maintains global consistency by computingan optimal alignment of the submap collection. By exploiting the underlying SDFrepresentation, we generate correspondence free constraints between submappairs that are computationally efficient enough to optimize the global problemeach time a new submap is added. We deploy the proposed system on a hexacopterMicro Aerial Vehicle (MAV) with an Intel i7-8650U CPU in two realisticscenarios: mapping a large-scale area using a 3D LiDAR, and mapping anindustrial space using an RGB-D camera. In the large-scale outdoor experiments,the system optimizes a 120x80m map in less than 4s and produces absolutetrajectory RMSEs of less than 1m over 400m trajectories. Our complete system,called voxgraph, is available as open source.

Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps