GY-SLAM:一个密集的语义SLAM系统用于工厂,工厂,运输机器人
Xiaolin Xie1,2, Yibo Qin2, Zhihong Zhang2
1Longmen Laboratory, Luoyang 471003, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了GY-SLAM,这是一个针对动态工厂环境优化的新型同时定位和映射 (SLAM) 系统. GY-SLAM提高了检测准确度,并降低了模型的复杂性,大大提高了智能机器人的本地化准确度和稳定性.
科学领域:
- 机器人和智能系统 机器人和智能系统
- 计算机视觉和机器学习
- 自主导航自主导航自主导航自主导航自主导航自主导航
背景情况:
- 同时定位和映射 (SLAM) 对智能机器人至关重要,但在动态环境中难以实现.
- 现有的SLAM系统在复杂,不断变化的环境中 (如工厂) 准确地绘制和定位机器人的局限性.
研究的目的:
- 开发一个先进的SLAM系统,GY-SLAM,适合动态的植物工厂运输环境.
- 为了提高机器人定位和实时映射的准确性,稳定性和效率.
主要方法:
- 基于YOLOv5的轻量级目标检测网络 (GY) 与GhostNet骨干,CoordConv,CARAFE和SE的集成.
- 开发GY-SLAM集成GY网络,以改善检测和降低模型复杂性.
- 构建纯静态八树图和网格图,以增强环境表示.
主要成果:
- GY-SLAM实现了95.364%的mAP@0.5,同时将参数降低了43.976%,计算成本降低了46.488%,模型大小降低了41.752%.
- 在动态场景中比ORB-SLAM3有显著的改进:92.59%的ATE RMSE改进,93.11%的RPE转换漂移RMSE改进,92.89%的RPE旋转漂移RMSE改进.
- 通过GY模型集成,系统检测速度提高了41.5944%,SLAM操作速度提高了17.7975%.
结论:
- GY-SLAM在动态环境中表现出卓越的性能,为机器人定位提供高精度和稳定性.
- 该系统的效率和实时功能支持在工厂和类似环境中实现物流任务的自动化.
- GY-SLAM为在充满挑战的动态环境中运行的智能机器人提供了具有竞争力和有效的解决方案.
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