DSOMF:一种基于机器学习的动态环境同时定位和映射技术
Shengzhe Yue1, Zhengjie Wang1, Xiaoning Zhang1
1School of Electromechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究介绍了一种使用实例分割和视频完成的动态场景SLAM技术,以提高机器人本地化在具有挑战性的环境中的准确性. 新方法增强了语义和几何数据的链接,以更好地跟踪和闭环,实现了超过85%的改进.
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:
- 经典的语义同时定位和映射 (SLAM) 算法在动态环境中与定位精度和地图完整性作斗争.
- 现有的方法往往无法准确地跟踪和映射随着时间的推移改变位置或外观的物体.
研究的目的:
- 开发一种新的动态场景SLAM技术,以提高定位精度和实时地图构建.
- 在具有显著物体运动和外观变化的环境中克服传统SLAM的局限性.
主要方法:
- 拟议的方法建立在直接稀疏计量 (DSO) 上,集成实例细分和视频完成算法.
- 它利用语义和几何数据融合进行强大的对象跟踪,并结合语义概率来改善图像匹配.
- 引入了一个循环关闭模块,利用静态背景上的视频 inpainting 来提高本地化可靠性.
主要成果:
- 动态场景SLAM技术在TUM和KITTI数据集以及无人驾驶平台上得到了验证.
- 实验结果显示,在各种动态场景中,与基线DSO系统相比,本地化准确度显著提高,超过85%.
- 该方法有效地处理动态元素,通过在中关联相同的对象,并利用完成的静态背景进行循环关闭.
结论:
- 开发的动态场景SLAM方法在具有挑战性的动态环境中显著提高了本地化准确性和地图质量.
- 集成实例细分,视频完成和语义数据融合为实时SLAM提供了强大的解决方案.
- 这项研究为在复杂的现实场景中运行的更可靠的自主导航系统提供了基础.
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