期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 3553-3560出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3061989
关键词
Mapping; motion and path planning
类别
资金
- SLAMcore Ltd.
- Imperial College President's Scholarship
- ESPRC ORCA Robotics Hub [EP/R026173/1]
- EPSRC Aerial ABM [EP/N018494/1]
- Imperial College London
- EPSRC [EP/N018494/1] Funding Source: UKRI
The proposed system utilizes adaptive-resolution volumetric mapping concept integrating with the hierarchical decomposition in an octree data structure, enabling fast collision queries for robot motion planning, and showing improvements in mapping accuracy and other aspects over existing techniques, particularly for high-resolution maps.
With the aim of bridging the gap between high quality reconstruction and robot motion planning, we propose an efficient system that leverages the concept of adaptive-resolution volumetric mapping, which naturally integrates with the hierarchical decomposition of space in an octree data structure. Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of occupancy probabilities in log-odds representation, which allows to represent both surfaces, as well as the entire free, i.e. observed space, as opposed to unobserved space. We introduce a method for choosing resolution -on the fly- in real-time by means of a multi-scale max-min pooling of the input depth image. The notion of explicit free space mapping paired with the spatial hierarchy in the data structure, as well as map resolution, allows for collision queries, as needed for robot motion planning, at unprecedented speed. We quantitatively evaluate mapping accuracy, memory, runtime performance, and planning performance showing improvements over the state of the art, particularly in cases requiring high resolution maps.
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