基于快速不变卡尔曼滤波的视觉惯性里程计  

Visual-inertial odometry based on fast invariant Kalman filter

作　　者：黄伟杰 张国山 HUANG Wei-jie;ZHANG Guo-shan(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学电气自动化与信息工程学院

出　　处：《控制与决策》2019年第12期2585-2593,共9页Control and Decision

基　　金：国家自然科学基金项目(61473202)

摘　　要：针对相机定位问题,设计基于深度相机和惯性传感器的视觉惯性里程计,里程计包含定位部分和重定位部分.定位部分使用不变卡尔曼滤波融合多层迭代最近点(ICP)的估计值和惯性传感器的测量值来获得精确的相机位姿,其中ICP的估计误差使用费舍尔信息矩阵进行量化.由于需要使用海量的点云作为输入,采用GPU并行计算以快速实现ICP估计和误差量化的过程.当视觉惯性里程计出现定位失败时,结合惯性传感器数据建立恒速模型,并基于此模型改进随机蕨定位方法,实现视觉惯性里程计的重定位.实验结果表明,所设计的视觉惯性里程计可以获得准确追踪相机且可以进行有效的重定位.This paper designs a visual-inertial odometry based on a depth camera and an inertial sensor for localizing the camera. The odometry contains camera localization and camera relocalization. Camera localization uses the invariant extended Kalman filter(IEKF) to fuse multilevel iteration closest point(ICP) estimates with the measurements from the inertial sensor for obtaining an accurate camera pose, in which, the estimated error of the multilevel ICP is quantized by the fisher information matrix. Because massive points are set as the inputs, GPU parallel computing is used to fast implement multilevel ICP estimation and its error quantization. When the odometry tracks camera failed, a constant velocity model is constructed with the data from the inertial sensor, and the random fern method is improved based on the velocity model to relocate the odometry. The experiment results show that the designed odometry can track the camera accurately and relocalize the camera effectively.

关 键 词：视觉惯性里程计 不变卡尔曼滤波 多层迭代最近点 随机蕨 GPU并行计算 惯性传感器 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]