Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations

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.

Cited by in corpus (7)

• Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization
• The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning
• Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation
• Simultaneous Scene Reconstruction and Whole-Body Motion Planning for Safe Operation in Dynamic Environments
• Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration
• DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds
• An Active Perception Game for Robust Exploration