The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning

  title={The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning},
  author={Rowan Border and Jonathan D. Gammell},
High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface… 



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

The Surface Edge Explorer (SEE) uses the density of current measurements to detect and explore observed surface boundaries and 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.

Next best view planning with an unstructured representation

Qualitative results show that both approaches are able to obtain highly complete observations of several scenes with varying size and structural complexity using multiple sensor modalities and the best performing strategies for addressing each of these challenges are integrated with SEE to create SEE++.

Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation

  • Rowan BorderJ. Gammell
  • Computer Science
    2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2020
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,

Surfel-Based Next Best View Planning

This letter explores a novel approach for NBV planning based on surfel representation of the environment that achieves better performance than volumetric algorithms based on ray casting implemented on graphics processing unit (GPU), with comparable results in terms of reconstruction quality.

Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects

The approach comprises the generation of next-best-scan (NBS) candidates and selection criteria, error minimization between scan patches and termination criteria, and iteratively determined by a boundary detection and surface trend estimation of the acquired model.

Efficient Constraint Evaluation Algorithms for Hierarchical Next-Best-View Planning

  • Kok-Lim LowA. Lastra
  • Computer Science
    Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
  • 2006
The algorithmic details of the hierarchical view evaluation are described, and efficient algorithms that evaluate sensing constraints and surface sampling densities between a view volume and a surface patch instead of simply between a single view point and asurface point are presented.

Volumetric Next-best-view Planning for 3D Object Reconstruction with Positioning Error

A next best view (NBV) algorithm that determines each view to reconstruct an arbitrary object and a method to deal with the uncertainty in sensor positioning is proposed.

View planning for 3D object reconstruction

A novel algorithm to select the next-best-view (NBV) for a range camera to model 3D arbitrary objects, using a volumetric representation and voxel labeling and two novel strategies to make faster the search of the NBV.

A comparison of volumetric information gain metrics for active 3D object reconstruction

This paper proposes several new ways to quantify the volumetric information (VI) contained in the voxels of a probabilisticvolumetric map, and compares them to the state of the art with extensive simulated experiments.