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

  title={Surface Edge Explorer (see): Planning Next Best Views Directly from 3D Observations},
  author={Rowan Border and Jonathan D. Gammell and Paul Newman},
  journal={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  • Rowan Border, J. Gammell, P. Newman
  • Published 23 February 2018
  • Computer Science, Engineering
  • 2018 IEEE International Conference on Robotics and Automation (ICRA)
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… 
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++.
Surface-driven Next-Best-View planning for exploration of large-scale 3D environments
A 3D reconstruction method based on TSDF (Truncated Signed Distance Function) mapping, which leverages the surfaces present in the environment to generate an informative exploration path for the UAV.
Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs
A novel cluster-based Next-Best-View path planning algorithm to simultaneously explore and inspect large-scale unknown environments with multiple Unmanned Aerial Vehicles (UAVs) and a nearest neighbor planner is proposed.
Informed Sampling Exploration Path Planner for 3D Reconstruction of Large Scenes
A novel informed sampling approach that leverages surface frontiers to sample viewpoints only where high information gain is expected, leading to faster exploration, and is shown to outperforms state-of-the-art exploration path planners in terms of both speed and reconstruction quality.
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization
A utility function that scores sets of viewpoints and avoids overlap between multiple sensors is proposed and it is shown that multi-sensor next-best-view planning with this utility function is an instance of submodular maximization under a matroid constraint.
Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction
This work proposes a 3D UAV path planning framework designed for detailed and complete small-scaled 3D reconstructions considering the semantic properties of the environment allowing for user-specified restrictions on the airspace.
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and
Active vision for 3D indoor scene reconstruction using a 3D camera on a pan-tilt mechanism
A novel approach to automatic indoor scene reconstruction from RGB-D images acquired from a single viewpoint using active vision is presented, designed to select the next view with sufficient information for reliable registration.
Where Should I Look? Optimised Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments
This work details the novel problem of determining the best head camera behaviour for mobile robots when constrained by a trajectory and proposes a greedy optimization-based solution that uses a combination of voxelised rewards and motion primitives.
DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds
This work proposes DeepSemanticHPPC, a novel uncertainty-aware hypothesis-based planner for unstructured environments that iteratively decreases semantic uncertainty along planned paths, filtering out unsafe paths with high confidence.


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.
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.
View planning for automated three-dimensional object reconstruction and inspection
This paper surveys and compares view planning techniques for automated 3D object reconstruction and inspection by means of active, triangulation-based range sensors and suggests adequate solutions to semiautomate the scan-register-integrate tasks.
Submodular Trajectory Optimization for Aerial 3D Scanning
An automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a high-fidelity 3D model, using a mathematical model of scene coverage that exhibits an intuitive diminishing returns property known as submodularity.
View/state planning for three-dimensional object reconstruction under uncertainty
We propose a holistic approach for three-dimensional (3D) object reconstruction with a mobile manipulator robot with an eye-in-hand sensor; considering the plan to reach the desired view/state, and
This work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally construct a topological network from the scene during the
Efficient algorithms for Next Best View evaluation
It is demonstrated that the most effective volumetric algorithm is a novel one that exploits spatial hierarchy, utilizes frontiers, and avoids redundant ray casting.
Online inspection path planning for autonomous 3D modeling using a micro-aerial vehicle
  • Soohwan Song, Sungho Jo
  • Engineering, Computer Science
    2017 IEEE International Conference on Robotics and Automation (ICRA)
  • 2017
An online inspection algorithm that consistently provides an optimal coverage path toward the next-best-view (NBV) in real time is proposed and outperforms the other approaches in both exploration and 3D modeling scenarios.
A Two-Stage Optimized Next-View Planning Framework for 3-D Unknown Environment Exploration, and Structural Reconstruction
The results and comparisons prove the system is able to autonomously explore the 3-D unknown environments and reconstruct the structural model with improved exploration efficiency in terms of path quality and total exploration time.
A sensor-based solution to the "next best view" problem
  • R. Pito
  • Computer Science
    Proceedings of 13th International Conference on Pattern Recognition
  • 1996
An algorithm is presented which solves the "next best view" (NBV) problem: determine the next position for the range scanner given its previous scans of the object, and which will work with nearly any range camera and scanning setup.