SOURCE-LINKED INTELLIGENCE

Semantic SLAM in Precision Agriculture using Bayesian Inference

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global