Submodular Trajectory Optimization for Aerial 3D Scanning

Roberts, Margaret; Shah, Shital; Dey, Debadeepta; Truong, Anh Minh; Sinha, Sudipta N.; Kapoor, Ashish; Hanrahan, Pat; Joshi, Neel

doi:10.1109/iccv.2017.569

Cited by 121 publications

(124 citation statements)

References 56 publications

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“…However, most of these methods focused mainly on planning and optimization of discrete viewpoints for visual information capturing, and then find a optimal/sub-optimal path to visit the selected viewpoints [16] [5] [22], or focused on coverage applications with continuous paths in simplified environment [23] [20]. Thus, these algorithms can not make full use of the video capturing features of the modern UAVs such that they are unsuitable or not performing well for our application.…”

Section: Relevant Workmentioning

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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Section: Relevant Workmentioning

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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“…Another application of our algorithms is in capturing images for 3D reconstruction of a scene. Since existing algorithms focus on computing robot paths to map a static scene [5], [4], our algorithms could be applied to persistently Fig. 5: The walks returned by Algorithm 3 to continually monitor the mausoleum of the Taj Mahal.…”

Section: B Persistent 3d Scene Reconstructionmentioning

confidence: 99%

Multi-Robot Routing for Persistent Monitoring with Latency Constraints

Asghar

Smith

Sundaram

2019

2019 American Control Conference (ACC)

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In this paper we study a multi-robot path planning problem for persistent monitoring of an environment. We represent the areas to be monitored as the vertices of a weighted graph. For each vertex, there is a constraint on the maximum time spent by the robots between visits to that vertex, called the latency, and the objective is to find the minimum number of robots that can satisfy these latency constraints. The decision version of this problem is known to be PSPACE-complete. We present a O(log ρ) approximation algorithm for the problem where ρ is the ratio of the maximum and the minimum latency constraints. We also present an orienteering based heuristic to solve the problem and show through simulations that in most of the cases the heuristic algorithm gives better solutions than the approximation algorithm. We evaluate our algorithms on large problem instances in a patrolling scenario and in a persistent scene reconstruction application. We also compare the algorithms with an existing solver on benchmark instances.

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“…Often times, the usage of these algorithms requires more computation power than available on current onboard computers of drones, which motivates the development of faster algorithms for the map densification. Another approach is the utilization of view‐planning methods (Roberts et al, ; Schmid et al, ), for instance, the approach (Mostegel, Rumpler, Fraundorfer, & Bischof, ) results in a set of images adapted to the challenging elements present in the scene, for example, by providing short baseline images for vegetation.…”

Section: Future Workmentioning

confidence: 99%

“…Another approach is the utilization of view-planning methods (Roberts et al, 2017;Schmid et al, 2012), for instance, the approach (Mostegel, Rumpler, Fraundorfer, & Bischof, 2016) results in a set of images adapted to the challenging elements present in the scene, for example, by providing short baseline images for vegetation.…”

Section: Future Workmentioning

confidence: 99%

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

Pestana

Maurer

Muschick

et al. 2019

Journal of Field Robotics

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Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size 100m×75m is created in ≈2.75min, therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach.

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Submodular Trajectory Optimization for Aerial 3D Scanning

Cited by 121 publications

References 56 publications

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

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

Multi-Robot Routing for Persistent Monitoring with Latency Constraints

Overview obstacle maps for obstacle‐aware navigation of autonomous drones

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