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Estimation, Search, and Planning (ESP) Research Group

Proactive estimation of occlusions and scene coverage for planning next best views in an unstructured representation

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

The process of planning views to observe a scene is known as the Next Best View (NBV) problem. Approaches often aim to obtain high-quality scene observations while reducing the number of views, travel distance and computational cost.

Considering occlusions and scene coverage can significantly reduce the number of views and travel distance required to obtain an observation. Structured representations (e.g., a voxel grid or surface mesh) typically use raycasting to evaluate the visibility of represented structures but this is often computationally expensive. Unstructured representations (e.g., point density) avoid the computational overhead of maintaining and raycasting a structure imposed on the scene but as a result do not proactively predict the success of future measurements.

This paper presents proactive solutions for handling occlusions and considering scene coverage with an unstructured representation. Their performance is evaluated by extending the density-based Surface Edge Explorer (SEE). Experiments show that these techniques allow an unstructured representation to observe scenes with fewer views and shorter distances while retaining high observation quality and low computational cost.

VideoVideo
PresentationPresentation
Publication Details
Type
Full-Paper-Refereed Conference Paper
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
arXiv Identifier arXiv
2009.04515 [cs.RO]
Manuscript
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BibTeX Entry
@inproceedings{border_iros20,
author = {Rowan Border and Jonathan D Gammell},
title = {Proactive estimation of occlusions and scene coverage for planning next best views in an unstructured representation},
booktitle = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
year = {2020},
month = {25--29 } # oct,
}