Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset

Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session mea...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 10; no. 8; pp. 8610 - 8617
Main Authors: Lei, Jiuzhou, Prabhu, Ankit, Liu, Xu, Cladera, Fernando, Mortazavi, Mehrad, Ehsani, Reza, Chaudhari, Pratik, Kumar, Vijay
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cameras
Costs
Feature extraction
Laser radar
Location awareness
Mapping
metric-semantic mapping
Monitoring
Point cloud compression
Robotics and automation in agriculture and forestry
Semantics
Three-dimensional displays
Time measurement
Topology
Visualization
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time. Our approach combines a LiDAR-RGB fusion module for 3D fruit localization with a 4D fruit association method leveraging positional, visual, and topology information for improved data association precision. Evaluated on real orchard data, our method achieves a 96.9% fruit counting accuracy for 1,790 apples across 60 trees, a mean fruit size estimation error of 1.1 cm, and a 23.7% improvement in 4D data association precision over baselines.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2025.3588037