摘要
针对目前大多数传统的视觉SLAM系统通常默认环境是静态的,但实际环境中是存在移动目标或者障碍物的,所以往往包含大量的误匹配点和动态点会导致系统定位精度降低的问题。提出一种基于ORB-SLAM3主体框架和深度学习技术的语义vSLAM系统,结合目标检测与光流法,旨在改进视觉SLAM系统在动态环境中的定位精度。首先,利用改进的YOLOX-S目标检测算法对潜在的动态目标进行识别;然后,利用几何法与光流法相结合精确检测异常值,并根据物体和人类运动状态不断调整动态包围盒的阈值。最终,保留动态框中包含静态框中的点,同时消除动态框中的其他点。在TUM和KITTI数据集上进行精确性的评估,实验结果表明,在数据集高动态序列下,与ORB-SLAM3,Crowd-SLAM比较,绝对轨迹均方根误差分别平均减少69.26%、16%,与DynaSLAM比较,在高动态场景中定位精度平均提升15%,这验证了在动态场景中提升了系统定位精度,此外,真实场景测试结果显示,该系统在各种动态环境中均表现出良好的性能。
Most traditional visual simultaneous localization and mapping(SLAM)systems typically assume a static environment;however,real-world environments often contain moving objects and obstacles,leading to a significant number of mismatched and dynamic points which can degrade localization accuracy.This paper proposes a semantic vSLAM system based on the ORB-SLAM3 framework and deep learning techniques,integrating object detection and optical flow methods to improve localization accuracy in dynamic environments.Firstly,an enhanced YOLOX-S object detection algorithm is utilized to identify potential dynamic targets.Subsequently,a combination of geometric and optical flow methods is employed to precisely detect outliers,with continuous adjustments to dynamic bounding box thresholds based on the motion states of objects and humans.Ultimately,points within static bounding boxes retained in dynamic frames are preserved,while others within dynamic frames are eliminated.The system′s accuracy is evaluated using the TUM and KITTI datasets.Experimental results demonstrate that under highly dynamic sequences,the proposed system achieves an average reduction of 69.26%and 16%in root mean square error of absolute trajectories compared to ORB-SLAM3 and Crowd-SLAM,respectively,and a 15%average improvement in localization accuracy in dynamic scenes when compared to DynaSLAM,thereby validating the enhanced system performance in dynamic environments.Moreover,the results of real-world scene tests demonstrate that the system performs well in various complex environments.
作者
程强
张友兵
周奎
Cheng Qiang;Zhang Youbing;Zhou Kui(College of Automotive Engineering,Hubei Automotive Industry Institute,Shiyan 442002,China;Sharing-X Key Joint Laboratory,College of Automotive Engineering,Shiyan 442002,China)
出处
《电子测量技术》
北大核心
2024年第23期123-133,共11页
Electronic Measurement Technology
基金
湖北省科技重大专项(2020AAA001)
湖北省重点研发计划项目(2021BED004)
湖北省武汉市科技重大专项(2022013702025184)资助。
