View planning for 3D shape reconstruction of buildings with unmanned aerial vehicles

Wei, Jing; Polden, Joseph; Tao, Pey Yuen; Lin, Wei; Shimada, Kenji

doi:10.1109/icarcv.2016.7838774

Cited by 21 publications

(18 citation statements)

References 17 publications

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“…In contrast to model-free exploration methods that focus on autonomy and real-time capability in unknown environments, model-based path planning algorithms rely on an available proxy model of the environment and focus on estimating a subsequent optimal path to maximize the coverage and accuracy of the object globally [10,11,[35][36][37][38]. In contrary to active modeling, these explore-and-exploit methods do not receive any feedback from the acquired images during the exploitation flight, which demands high attention to the applied heuristics being used for generating the refinement path.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the execution of the optimized path is easy and fast for any kind of UAV by simply navigating alongside the optimized waypoints. The prior model can either be based on an existing map with height information [36] or is generated by photogrammetric reconstructions from a preceding manual flight at a safe altitude or via standard flight planning methods (e.g., regular grids or circular trajectories) [10,11] and is usually expressed by a set of discrete 3D points in a voxel space [10,11,37,40] or by volumetric surfaces, such as triangulated meshes [35,36,38,41]. In order to define appropriate views for the optimized trajectory, camera viewpoint hypotheses are either regularly sampled in the free 3D airspace [10,37] resulting in 3D camera graphs, or are sparsely sampled in a 2D view manifold [38] or in skeleton sets [42] around the object.…”

Section: Related Workmentioning

confidence: 99%

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Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction

2019

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Small-scaled unmanned aerial vehicles (UAVs) emerge as ideal image acquisition platforms due to their high maneuverability even in complex and tightly built environments. The acquired images can be utilized to generate high-quality 3D models using current multi-view stereo approaches. However, the quality of the resulting 3D model highly depends on the preceding flight plan which still requires human expert knowledge, especially in complex urban and hazardous environments. In terms of safe flight plans, practical considerations often define prohibited and restricted airspaces to be accessed with the vehicle. We propose 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. The generated trajectories account for the desired model resolution and the demands on a successful photogrammetric reconstruction. We exploit semantics from an initial flight to extract the target object and to define restricted and prohibited airspaces which have to be avoided during the path planning process to ensure a safe and short UAV path, while still aiming to maximize the object reconstruction quality. The path planning problem is formulated as an orienteering problem and solved via discrete optimization exploiting submodularity and photogrammetrical relevant heuristics. An evaluation of our method on a customized synthetic scene and on outdoor experiments suggests the real-world capability of our methodology by providing feasible, short and safe flight plans for the generation of detailed 3D reconstruction models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction

2019

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“…The flying distances computed by the proposed method in this paper are 425.6m and 466.2m for the two target structures, respectively. The distances of the inspection paths are 507.7m and 587.5m using the previous methods [5][11] that solves the TSP after applying the viewpoint-based methods to find the viewpoints (labeled as VPP-TSP). The discrete viewpoints based greedy method, a simple but robust baseline [15][11] that iteratively adds the most cost effective viewpoints, planned the path with the traveling distance of 531.0m and 687.1m.…”

Section: A Setupmentioning

confidence: 99%

“…• a novel sampling-based coverage planning framework that is natively suitable for model-based, continuous CPP problem with UAV visual inspection, improved from our previous discrete viewpoint-based planning framework [1] [5]. • flexible and reconfigurable design of the planning framework such that the modules (e.g.…”

Section: Introductionmentioning

confidence: 99%

Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection

Wei¹,

Deng

Xiao

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Planning the path to gather the surface information of the target objects is crucial to improve the efficiency of and reduce the overall cost, for visual inspection applications with Unmanned Aerial Vehicles (UAVs). Coverage Path Planning (CPP) problem is often formulated for these inspection applications because of the coverage requirement. Traditionally, researchers usually plan and optimize the viewpoints to capture the surface information first, and then optimize the path to visit the selected viewpoints. In this paper, we propose a novel planning method to directly sample and plan the inspection path for a camera-equipped UAV to acquire visual and geometric information of the target structures as a video stream setting in complex 3D environment. The proposed planning method first generates via-points and path primitives around the target object by using sampling methods based on voxel dilation and subtraction. A novel Primitive Coverage Graph (PCG) is then proposed to encode the topological information, flying distances, and visibility information, with the sampled via-points and path primitives. Finally graph search is performed to find the resultant path in the PCG to complete the inspection task with the coverage requirements. The effectiveness of the proposed method is demonstrated through simulation and field tests in this paper.

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“…[7] discusses the setup of a UAV outdoors but adding an obstacle avoiding system. Similarly, [8] employs a freely available 2-D map of buildings in order to construct a rough 3-D model and inspect and refine it with a UAV. Bircher et al [9] focus on structural inspection in their work, and they employ a two-step optimization paradigm to find good viewpoints.…”

Section: Related Workmentioning

confidence: 99%

On the use of unmanned aerial vehicles for autonomous object modeling

Welle

Ericson

Ambruş

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

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Abstract-In this paper we present an end to end object modeling pipeline for an unmanned aerial vehicle (UAV). We contribute a UAV system which is able to autonomously plan a path, navigate, acquire views of an object in the environment from which a model is built. The UAV does collision checking of the path and navigates only to those areas deemed safe. The data acquired is sent to a registration system which segments out the object of interest and fuses the data. We also show a qualitative comparison of our results with previous work.

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View planning for 3D shape reconstruction of buildings with unmanned aerial vehicles

Cited by 21 publications

References 17 publications

Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction

Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction

Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection

On the use of unmanned aerial vehicles for autonomous object modeling

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