Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps
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Date
2020-01
Publication Type
Journal Article
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yes
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Abstract
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.
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published
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Journal / series
Volume
5 (1)
Pages / Article No.
227 - 234
Publisher
IEEE
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Methods
Software
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Date collected
Date created
Subject
SLAM; Aerial Systems; Perception and Autonomy; Mapping
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.