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一种在恶劣环境下基于立体相机/微机电系统惯性测量单元(MEMS-IMU)的新型容错导航与定位方法。

A Novel Fault-Tolerant Navigation and Positioning Method with Stereo-Camera/Micro Electro Mechanical Systems Inertial Measurement Unit (MEMS-IMU) in Hostile Environment.

作者信息

Yuan Cheng, Lai Jizhou, Lyu Pin, Shi Peng, Zhao Wei, Huang Kai

机构信息

Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China.

Key Laboratory of Internet of Things and Control Technology in Jiangsu province, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China.

出版信息

Micromachines (Basel). 2018 Nov 27;9(12):626. doi: 10.3390/mi9120626.

Abstract

Visual odometry (VO) is a new navigation and positioning method that estimates the ego-motion of vehicles from images. However, VO with unsatisfactory performance can fail severely in hostile environment because of the less feature, fast angular motions, or illumination change. Thus, enhancing the robustness of VO in hostile environment has become a popular research topic. In this paper, a novel fault-tolerant visual-inertial odometry (VIO) navigation and positioning method framework is presented. The micro electro mechanical systems inertial measurement unit (MEMS-IMU) is used to aid the stereo-camera, for a robust pose estimation in hostile environment. In the algorithm, the MEMS-IMU pre-integration is deployed to improve the motion estimation accuracy and robustness in the cases of similar or few feature points. Besides, a dramatic change detector and an adaptive observation noise factor are introduced, tolerating and decreasing the estimation error that is caused by large angular motion or wrong matching. Experiments in hostile environment showing that the presented method can achieve better position estimation when compared with the traditional VO and VIO method.

摘要

视觉里程计(VO)是一种新的导航和定位方法,它从图像中估计车辆的自身运动。然而,性能不佳的VO在恶劣环境中可能会严重失效,因为特征较少、角运动速度快或光照变化。因此,提高VO在恶劣环境中的鲁棒性已成为一个热门的研究课题。本文提出了一种新颖的容错视觉惯性里程计(VIO)导航和定位方法框架。微机电系统惯性测量单元(MEMS-IMU)用于辅助立体相机,以便在恶劣环境中进行鲁棒的姿态估计。在该算法中,部署了MEMS-IMU预积分,以提高在特征点相似或较少情况下的运动估计精度和鲁棒性。此外,引入了一个剧烈变化检测器和一个自适应观测噪声因子,以容忍和减少由大角度运动或错误匹配引起的估计误差。在恶劣环境中的实验表明,与传统的VO和VIO方法相比,本文提出的方法能够实现更好的位置估计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f751/6315599/5531d71368af/micromachines-09-00626-g001.jpg

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