Under-Canopy Navigation Using Aerial Lidar Maps

Autonomous navigation in unstructured natural environments poses a significant challenge. In goal navigation tasks without prior information, the limited look-ahead of onboard sensors utilised by robots compromises path efficiency. We propose a novel approach that leverages an above-the-canopy aeria...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2024-08, Vol.9 (8), p.7031-7038
Main Authors: Carvalho de Lima, Lucas, Lawrance, Nicholas, Khosoussi, Kasra, Borges, Paulo Vinicius Koerich, Bruenig, Michael
Format: Article
Language:English
Subjects:
Ablation
Algorithms
Autonomous navigation
Canopies
Cost function
Field robots
Laser radar
Lidar
mapping
Navigation
Path planning
Planning
Robot dynamics
Robot sensing systems
robotics and automation in agriculture and forestry
Sensors
Trajectory
Uncertainty
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Summary:Autonomous navigation in unstructured natural environments poses a significant challenge. In goal navigation tasks without prior information, the limited look-ahead of onboard sensors utilised by robots compromises path efficiency. We propose a novel approach that leverages an above-the-canopy aerial map for improved ground robot navigation. Our system utilises aerial lidar scans to create a 3D probabilistic occupancy map, uniquely incorporating the uncertainty in the aerial vehicle's trajectory for improved accuracy. Novel path planning cost functions are introduced, combining path length with obstruction risk estimated from the probabilistic map. The D* Lite algorithm then calculates an optimal (minimum-cost) path to the goal. This system also allows for dynamic replanning upon encountering unforeseen obstacles on the ground. Extensive experiments and ablation studies in simulated and real forests demonstrate the effectiveness of our system.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3417115