基于贝叶斯推理的精准农业语义SLAM

中文摘要 AI 辅助

本文提出了一种专用于精准农业中自主机器人的实时语义世界建模框架。该框架将对象及其语义属性的概率映射（通过贝叶斯推理更新）与基于图的同时定位与映射（SLAM）方法相结合，后者使用$g^2o$（一种通用的图优化框架）实现。这种集成使得在不完全依赖GPS的情况下也能实现精确的映射和定位。通过利用植物类型、大小和健康状态等语义信息，机器人可以在作物田间执行任务的同时进行映射和自身定位。所提出的框架通过Gazebo仿真以及使用波士顿动力公司机器狗Spot在室内人工植物田地上进行的物理实验得到了验证。训练了一个YOLOv8n目标检测模型，用于从深度相机观测中提取对象和语义数据。这些仿真和实验表明，该系统能够成功地对至少400株植物进行实时映射。

英文摘要

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.

发表机构

• Eindhoven University of Technology（埃因霍温理工大学）

机构由 AI 辅助整理，请以论文原文为准。