目前的同时定位与地图构建(Simultaneous Localization and Mapping,SLAM)研究大多是基于静态场景的假设,而实际生活中动态物体是不可避免的.在视觉SLAM系统中加入深度学习,可以协同剔除场景中的动态物体,有效提升视觉SLAM在动态环境下的鲁棒性.文章首先介绍了动态环境下基于深度学习的视觉SLAM分类,然后详细介绍了基于目标检测、基于语义分割和基于实例分割的视觉SLAM,并对它们进行了分析比较.最后,结合近年来视觉SLAM的发展趋势,通过对动态环境下基于深度学习的视觉SLAM存在的主要问题进行分析,总结了未来可能的发展方向.
Review of Visual SLAM Research Based on Deep Learning in Dynamic Environments
The current research on simultaneous localization and mapping(SLAM)in academia mostly assumes static scenes,but dynamic objects are inevitable in real-life scenarios.Integrating deep learning into visual SLAM systems can collaboratively eliminate dynamic objects from the scene,effectively enhancing the robustness of visual SLAM in dynamic environments.This paper first introduces a classification of deep learning-based visual SLAM in dynamic environments and then provides a detailed overview of visual SLAM systems based on object detection,semantic segmentation,and instance segmentation.A comparative analysis of these approaches is also presented.Finally,considering the recent trends in the development of visual SLAM,the paper analyzes the main challenges of deep learning-based visual SLAM in dynamic environments and summarizes potential future directions.
visual SLAMdeep learningdynamic environmentsemantic information
NETL
NSTL
万方数据
