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GNV2-SLAM:用于牛棚检查机器人的视觉SLAM系统

Xinwu Du^1,2,3, Tingting Li², Xin Jin²

¹Longmen Laboratory, Luoyang, China.

Frontiers in robotics and AI

|October 6, 2025

概括

本研究介绍了GNV2-SLAM,这是一个增强的同时定位和映射 (SLAM) 系统,用于在牛棚等动态环境中的自主机器人. 它显著提高了定位准确性和稳定性,为自动化检查任务提供实时性能.

科学领域:

机器人和人工智能机器人和人工智能
计算机视觉计算机视觉
机器学习机器学习

背景情况:

同时定位和映射 (SLAM) 对于自主机器人导航至关重要.
传统的SLAM系统在动态环境中扎,原因是功能丢失等问题.
牛棚检查需要强大而准确的自主导航系统.

研究的目的:

开发一种创新的SLAM系统,GNV2-SLAM,适用于动态环境,特别是用于牛棚检查.
提高SLAM系统的准确性,稳定性和实时性能.
将一个轻量级但准确的对象检测网络集成到SLAM框架中.

主要方法:

基于ORB-SLAM2的拟议GNV2-SLAM系统,包含一个轻量级的GNV2物体检测网络 (基于YOLOv8) 与GhostNetv2骨干,CBAM注意力和SCDown下采样.
实施了点和线特征提取技术,以处理动态目标和模糊图像.
在TUM数据集和现实世界牛棚环境中评估性能.

主要成果:

GNV2网络实现了95.19%的mAP@0.5,参数减少了41.95%,计算成本减少了36.71%,模型大小减少了40.44%.
在动态环境中,GNV2-SLAM显示了相对于ORB-SLAM2的显著改进,绝对轨迹误差 (ATE) RMSE减少了96.13%.
在30毫秒以下的单处理中实现实时性能,并在牛棚试验中显示出卓越的轨迹一致性.

结论:

在动态环境中,GNV2-SLAM为自主导航提供了强大而准确的解决方案,其性能优于传统方法.
该系统在物体检测和特征提取方面的效率提高了其适用于自动化牛棚检查等任务的适用性.
GNV2-SLAM提供了一种具有竞争力的技术解决方案,用于推进农业检查任务的自动化.

关键词:

在GNV2-SLAM中使用.斯拉姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯这就是YOLOv8的意义.计算机视觉计算机视觉牛棚检查牛棚检查

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