PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction

Chen, Feng; Li, Haojia; Gao, Fei; Zhou, Boyu; Shen, Shaojie

doi:10.48550/arxiv.2302.04488

Cited by 2 publications

(3 citation statements)

References 30 publications

(52 reference statements)

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“…However, the framework in [10] was tuned to the best of our capability to ensure an overall better performance in the simulated urban world. Moreover, implementation of the related state-of-art open-source repositories [24,40] with UnrealEngine [41] and AirSim [42] is currently incompatible with our established Gazebo [35] and ROS [34] setup. structures mentioned prior in Fig.…”

Section: Performance Comparisonmentioning

confidence: 99%

Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach

Viswanathan,

Lindqvist,

Satpute

et al. 2023

J Intell Robot Syst

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This paper highlights the significance of maintaining and enhancing situational awareness in Urban Search and Rescue (USAR) missions. It focuses specifically on investigating the capabilities of Unmanned Aerial Vehicles (UAV) equipped with limited sensing capabilities and onboard computational resources to perform visual inspections of apriori unknown fractured and collapsed structures in unfamiliar environments. The proposed approach, referred to as First Look Inspect-Explore (FLIE), employs a flexible bifurcated behavior tree that leverages real-time RGB image and depth cloud data. By employing a recursive and reactive synthesis of safe view pose within the inspection module, FLIE incorporates a novel active visual guidance scheme for identifying previously inspected surfaces. Furthermore, the integration of a tiered hierarchical exploration module with the visual guidance system enables the UAV to navigate towards new and unexplored structures without relying on a map. This decoupling reduces memory overhead and computational effort by eliminating the need to plan based on an incrementally built, error-prone global map. The proposed autonomy is extensively evaluated through simulation and experimental verification under various scenarios and compared against state-of-art approaches, demonstrating its performance and effectiveness.

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Section: Performance Comparisonmentioning

confidence: 99%

Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach

Viswanathan,

Lindqvist,

Satpute

et al. 2023

J Intell Robot Syst

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“…In the context of unknown scenarios lacking prior information, path planning for scene reconstructions often relies on widely adopted strategies such as the Next-Best-View (NBV) strategy [15][16][17][18][19][20] and the "explore-and-exploit" strategy [7][8][9][10][11][12][13].…”

Section: Path Planning For Scene Reconstructionmentioning

confidence: 99%

“…Zhou et al [19] presented a layered framework that employs a frontier information structure to systematically search for a path that covers the entire scene. Feng et al [20] introduced a coarse structure prediction module, which enables them to plan a trajectory at a local level, thereby optimizing the reconstruction quality. However, all of these methods necessitate real-time onboard processing and rely on costly equipment capable of performing real-time depth computation.…”

Section: Nbv Strategymentioning

confidence: 99%

Multi-UAV Cooperative and Continuous Path Planning for High-Resolution 3D Scene Reconstruction

Sui,

Zhang,

Gou

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive capture of multi-view images. Furthermore, the limited performance and battery capacity of a single UAV hinder efficient image capturing of large-scale scenes. To address these challenges, this paper presents a novel method for multi-UAV continuous trajectory planning aimed at the image captures and reconstructions of a scene. Our primary contribution lies in the development of a path planning framework rooted in task and search principles. Within this framework, we initially ascertain optimal task locations for capturing images by assessing scene reconstructability, thereby enhancing the overall quality of reconstructions. Furthermore, we curtail energy costs of trajectories by allocating task sequences, characterized by minimal corners and lengths, among multiple UAVs. Ultimately, we integrate considerations of energy costs, safety, and reconstructability into a unified optimization process, facilitating the search for optimal paths for multiple UAVs. Empirical evaluations demonstrate the efficacy of our approach in facilitating collaborative full-scene image captures by multiple UAVs, achieving low energy costs while attaining high-quality 3D reconstructions.

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PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction

Cited by 2 publications

References 30 publications

Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach

Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach

Multi-UAV Cooperative and Continuous Path Planning for High-Resolution 3D Scene Reconstruction

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