基于轻量化目标检测网络的RGB-D视觉SLAM系统

A RGB-D visual SLAM system based on lightweight object detection network

RGB-D SLAM是一种利用深度相机实现同时定位和地图构建的技术。传统的视觉SLAM系统基于对静态环境的假设,然而实际环境中往往存在动态物体,这可能导致SLAM系统的位姿估计出现显著的偏差。针对这一问题,提出了基于轻量化的YOLOv8s目标检测的RGB-D视觉SLAM系统,采用Socket通信方式,将目标检测结果传给SLAM,然后利用Depth Value-RANSAC几何算法剔除检测框内的动态特征点,提高了SLAM系统在动态环境中的定位精度。实验使用TUM数据集进行验证,结果表明,本文系统精度相比ORB-SLAM2有明显提高。与其他SLAM系统相比,本文系统在精度和实时性上有不同程度的改进。

RGB-D SLAM is a technology that utilizes depth cameras to achieve simultaneous localization and mapping(SLAM).Traditional visual SLAM systems are based on the assumption of a static environment,yet dynamic objects often exist in real-world scenarios,potentially leading to significant deviations in the pose estimation of SLAM systems.To address this issue,this paper proposes a SLAM system based on lightweight YOLOv8s object detection.This system employs Socket communication to transmit object detection results to the SLAM system,which then utilizes the Depth Value-RANSAC geometric algorithm to eliminate dynamic feature points within the detected bounding boxes,thereby enhancing the positioning accuracy of the SLAM system in dynamic environments.The experiments were conducted using the TUM dataset for validation,and the results indicate that the system s accuracy is significantly improved compared to ORB-SLAM2.Compared to other SLAM systems,varying degrees of improvement in accuracy and real-time performance were observed.