Large-Scale Autonomous Flight With Real-Time Semantic SLAM Under Dense Forest Canopy

Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments...

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Vydáno v:IEEE robotics and automation letters Ročník 7; číslo 2; s. 5512 - 5519
Hlavní autoři: Liu, Xu, Nardari, Guilherme V., Cladera, Fernando, Tao, Yuezhan, Zhou, Alex, Donnelly, Thomas, Qu, Chao, Chen, Steven W., Romero, Roseli A. F., Taylor, Camillo J., Kumar, Vijay
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Aerial systems: perception and autonomy
Autonomous aerial vehicles
Autonomous navigation
Autonomy
Canopies
Computational modeling
Control stability
Data models
Drift
field robotics
Forestry
Ground plane
Mapping
Planning
Real time
Real-time systems
robotics and automation in agriculture and forestry
Semantics
Simultaneous localization and mapping
SLAM
Stability analysis
Trajectory
Unmanned aerial vehicles
Unstructured data
ISSN:2377-3766, 2377-3766
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Shrnutí:Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments. In this letter, we propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. The autonomous navigation module utilizes a multi-level planning and mapping framework and computes dynamically feasible trajectories that lead the UAV to build a semantic map of the user-defined region of interest in a computationally and storage efficient manner. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability. This leads the UAV to execute its mission accurately and safely at scale.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3154047