Proactive Occlusion and Coverage Estimation for NBV Planning (IROS 2020)
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Rowan has a great new paper on how to estimate occlusions and view coverage in density-based next best view (NBV) algorithms at IROS 2020. The full paper is already available on arXiv and you can also download the code to try it yourself.
- Publication
- Conference
- Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Pages
- 4219–4226
- 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.