Authors: Victor Reijgwart*, Alexander Millane*, Helen Oleynikova, Roland Siegwart, Cesar Cadena, Juan Nieto
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 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 RMSE of less than 1m over 400m trajectories. Our complete system, called voxgraph, is available as open source (https://github.com/ethz-asl/voxgraph).
- Published in: IEEE Robotics and Automation Letters (Volume: 5, Issue: 1, Jan. 2020)
- DOI: 10.1109/LRA.2019.2953859
- Read paper
- Date: 2019