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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Published in:	IEEE robotics and automation letters Vol. 7; no. 2; pp. 5512 - 5519
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Liu, Xu Tao, Yuezhan Taylor, Camillo J. Kumar, Vijay Donnelly, Thomas Zhou, Alex Nardari, Guilherme V. Qu, Chao Cladera, Fernando Romero, Roseli A. F. Chen, Steven W.
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SubjectTerms	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
Title	Large-Scale Autonomous Flight With Real-Time Semantic SLAM Under Dense Forest Canopy
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