Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations
This addresses the challenge of autonomous 3D scene modeling in robotics, offering a more efficient alternative to existing volumetric and surface-based methods, though it appears incremental as it builds on prior NBV planning techniques.
The paper tackles the problem of Next Best View (NBV) planning for 3D scene surveying by introducing a scene-model-free approach using density representation, which experimentally provides better surface coverage in lower computation time than state-of-the-art volumetric methods while moving equivalent distances.
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.