Semantic SLAM in Precision Agriculture using Bayesian Inference

原文：Semantic SLAM in Precision Agriculture using Bayesian Inference

作者：Ruben Beumer, Sander Doodeman, René van de Molengraft, Duarte Antunes

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2609.20604v1 (cs)

[Submitted on 17 Sep 2026]

Title:Semantic SLAM in Precision Agriculture using Bayesian Inference

Authors:Ruben Beumer, Sander Doodeman, René van de Molengraft, Duarte Antunes

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Abstract:This paper presents a real-time semantic world modeling framework specialized for precision agriculture using autonomous robots. The framework combines probabilistic mapping of objects and their semantic attributes, updated through Bayesian inference, with a graph-based Simultaneous Localization and Mapping (SLAM) approach implemented using $g^2o$, a general framework for graph optimization. This integration enables accurate mapping and localization without relying solely on GPS. By leveraging semantic information such as plant type, size, and health, the robot can perform tasks while mapping and localizing itself within a field of crops. The proposed framework was validated through Gazebo simulations and physical experiments on an indoor field with artificial plants using Boston Dynamics’ robot dog Spot. A YOLOv8n object detection model was trained to extract object and semantic data from depth camera observations. These simulations and experiments demonstrate that the system can successfully perform real-time mapping of up to at least 400 plants.

Subjects:

Robotics (cs.RO)

Cite as:

arXiv:2609.20604 [cs.RO]

(or

arXiv:2609.20604v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.20604

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arXiv-issued DOI via DataCite (pending registration)

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机器人

由「前沿雷达」于 2026-09-20 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。