Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps
- Journal Article
Rechte / LizenzIn Copyright - Non-Commercial Use Permitted
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 i7-8650 U 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 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph, is available as open source. Mehr anzeigen
Zeitschrift / SerieIEEE Robotics and Automation Letters
Seiten / Artikelnummer
ThemaSLAM; Aerial Systems; Perception and Autonomy; Mapping
Organisationseinheit03737 - Siegwart, Roland Y. / Siegwart, Roland Y.