一个语义拓图表来检测在动态环境中的可视同步定位和映射系统的重新定位和循环关闭
Yang Wang1, Yi Zhang2, Lihe Hu1
1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了一个语义拓图表,以改善动态环境中的机器人本地化和映射. 该方法提高了视觉SLAM系统的重新定位和循环闭合检测精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 传统的视觉SLAM (VSLAM) 在动态环境中由于静态场景假设而扎.
- 在重新定位和循环关闭检测中的精度降低发生在VSLAM在现实场景中丢失时.
- 动态环境对移动机器人导航和绘图构成重大挑战.
研究的目的:
- 为VSLAM提出一种新的重新定位和循环关闭检测方法.
- 在动态环境中增强同时定位和映射 (SLAM) 的稳定性.
- 通过语义理解来提高移动机器人导航的准确性.
主要方法:
- 使用YOLOv5进行对象检测,以区分动态和静态对象.
- 基于静态对象的空间位置的语义拓图的构建.
- 开发一种加权拓图形相似度衡,用于关键框架比较.
主要成果:
- 提出的语义拓图表方法显著提高了重新定位的正确性.
- 在动态环境中实现了循环关闭检测的更高准确性.
- 在公共数据集上的实验验验证了语义拓图形方法的有效性.
结论:
- 语义拓图有效地解决了动态设置中的传统VSLAM的局限性.
- 这种方法为移动机器人导航和绘图提供了更可靠的解决方案.
- 该方法为准确的定位和循环关闭检测提供了一个强大的框架.
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