基于惯性先验校正图像灰度的VIO前端改良方法  

An improved VIO front-end method based on inertial prior correction of image grayscale

作　　者：徐锦乐 潘树国[1] 高旺 刘宏 王向 XU Jinle;PAN Shuguo;GAO Wang;LIU Hong;WANG Xiang(College of Instrument Science and Engineering,Southeast University,Nanjing 210096,China)

机构地区：[1]东南大学仪器科学与工程学院,南京210096

出　　处：《全球定位系统》2023年第3期102-109,共8页Gnss World of China

基　　金：国家重点研发计划(2021YFB3900804)。

摘　　要：针对封闭环境中不同光照场景导致相机图像序列光照过强、过弱以及光照强度大幅变化等问题,提出了一种基于惯性先验预测并校正图像灰度、提升光流跟踪成功率的视觉惯性里程计(VIO)前端改良方法,以解决由于极端光照条件导致的特征点跟踪失败问题.该VIO前端改良算法通过以惯性预测特征点像素位置,并衡量图像序列中对应特征点邻域之间的相似性对当前图像灰度进行伽马校正.通过TUM数据集实验验证,所提算法定位精度比VINS-Mono平均提升了17.7%;在真实的室内外环境下进行实验,相较于VINS-Mono有更好的表现.Aiming at the problems of camera image sequences with excessive or weak illumination and significant changes in illumination intensity due to different lighting scenarios in a closed environment,a visual-inertial odometer(VIO)system based on inertial prior prediction to enhance images was proposed to solve the problem of features tracking loss due to extreme lighting conditions.This image enhancement algorithm performs grayscale gamma correction on the current image by predicting the pixel positions of feature points using inertia and associating the grayscale relationships between the corresponding feature point neighborhoods in the front and rear images.Experimental verification with TUM dataset shows that the proposed algorithm has an average improvement of 17.7%in positioning accuracy compared to VINS-Mono.Experiments in real indoor and outdoor environments also show better performance compared to VINS-Mono.

关 键 词：即时定位与地图构建(SLAM) 视觉惯性里程计(VIO) 图像增强 灰度校正 特征跟踪 

分 类 号：P228.4[天文地球—大地测量学与测量工程]