动态环境下基于线特征的RGB-D视觉里程计

RGB-D Visual Odometry in Dynamic Environments Using Line Features

基于RGB-D的视觉SLAM(同时定位与建图)算法基本都假设环境是静态的,然而在实际环境中经常会出现动态物体,导致SLAM算法性能的下降.为此,本文提出一种基于线特征的RGB-D视觉里程计方法,通过计算直线特征的静态权重来剔除动态直线特征,并根据剩余的直线特征估计相机位姿.本文方法既可以减小动态物体的影响,又能避免点特征过少而导致的跟踪失效.公共数据集实验结果表明,与现有的基于ORB(orientedFAST and rotated BRIEF)点特征的方法相比,本文方法减小了动态环境下的跟踪误差约30%,提高了视觉里程计在动态环境下的精度和鲁棒性.

Most of RGB-D SLAM (simultaneous localization and mapping)methods assume that the environments are static.However,there are often dynamic objects in real world environments,which can degrade the SLAM performance. In order to solve this problem,a line feature-based RGB-D (RGB-depth)visual odometry is proposed.It calculates static weights of line features to filter out dynamic line features,and uses the rest of line features to estimate the camera pose.The proposed method not only reduces the influence of dynamic objects,but also avoids the tracking failure caused by few point features.The experiments are carried out on a public dataset.Compared with state-of-the-art methods like ORB (oriented FAST and rotated BRIEF)method,the results demonstrate that the proposed method reduces the tracking error by about 30% and improves the accuracy and robustness of visual odometry in dynamic environments.