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Estimation, Search, and Planning (ESP) Research Group

Surface Edge Explorer (SEE): Planning next best views directly from 3D observations

Authors
  1. Rowan Border
  2. Jonathan D. Gammell
  3. Paul Newman
Publication Date
Abstract

Surveying 3D scenes is a common task in robotics. Systems can do so autonomously by iteratively obtaining measurements. This process of planning observations to improve the model of a scene is called Next Best View (NBV) planning.

NBV planning approaches often use either volumetric (e.g., voxel grids) or surface (e.g., triangulated meshes) representations. Volumetric approaches generalise well between scenes as they do not depend on surface geometry but do not scale to high-resolution models of large scenes. Surface representations can obtain high-resolution models at any scale but often require tuning of unintuitive parameters or multiple survey stages.

This paper presents a scene-model-free NBV planning approach with a density representation. The Surface Edge Explorer (SEE) uses the density of current measurements to detect and explore observed surface boundaries. This approach is shown experimentally to provide better surface coverage in lower computation time than the evaluated state-of-the-art volumetric approaches while moving equivalent distances.

VideoVideo
PresentationPresentation
Publication Details
Type
Full-Paper-Refereed Conference Paper
Conference
IEEE International Conference on Robotics and Automation (ICRA)
Location
Brisbane, Australia
Pages
1–8
Digital Object Identifier DOI
10.1109/ICRA.2018.8461098
arXiv Identifier arXiv
1802.08617 [cs.RO]
Manuscript
Google Scholar Google Scholar
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BibTeX Entry
@inproceedings{border_icra18,
author = {Rowan Border and Jonathan D Gammell and Paul Newman},
title = {Surface {Edge} {Explorer} ({SEE}): {Planning} next best views directly from {3D} observations},
booktitle = {Proceedings of the {IEEE} International Conference on Robotics and Automation ({ICRA})},
year = {2018},
pages = {1--8},
address = {Brisbane, Australia},
month = {21--25 } # may,
doi = {10.1109/ICRA.2018.8461098},
}