  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.
