Moussa Mohamed, Zahran Shady, Mostafa Mostafa, Moussa Adel, El-Sheimy Naser, Elhabiby Mohamed
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Department of Electrical and Computer Engineering, Port-Said University, Port-Said 42526, Egypt.
Sensors (Basel). 2020 Nov 17;20(22):6567. doi: 10.3390/s20226567.
Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This proposed system is based on optical flow and multiple mass flow sensors integrations to aid the low-cost INS by providing the navigation extended Kalman filter (EKF) with forward velocity and change of heading updates to enhance the vehicle navigation. The optical flow is computed for frames taken using a consumer portable device (CPD) camera mounted in the upward-looking direction to avoid moving objects in front of the camera and to exploit the typical features of the underground parking or tunnels such as ducts and pipes. On the other hand, the multiple mass flow sensors measurements are modeled to provide forward velocity information. Moreover, a mass flow differential odometry is proposed where the vehicle change of heading is estimated from the multiple mass flow sensors measurements. This integrated aiding system can be used for unmanned aerial vehicles (UAV) and land vehicle navigations. However, the experimental results are implemented for land vehicles through the integration of CPD with mass flow sensors to aid the navigation system.
如今，自动驾驶车辆在导航、周围环境感知和控制方面引发了大量研究兴趣。全球导航卫星系统/惯性导航系统（GNSS/INS）是任何车辆导航系统的重要组成部分之一。然而，GNSS在某些运行场景中存在局限性，例如在城市区域和室内环境中，GNSS信号会受到多径效应或信号中断的影响。另一方面，INS独立导航解决方案会因INS误差而随时间退化。因此，现代车辆导航系统依赖于不同传感器之间的集成，以在GNSS信号中断期间辅助INS减轻其漂移。然而，辅助传感器存在一些挑战，包括价格高昂、计算成本高以及受环境和天气影响。本文提出了一种适用于室内环境（如地下停车场）车辆的集成辅助导航系统。该系统基于光流和多个质量流量传感器的集成，通过为导航扩展卡尔曼滤波器（EKF）提供前进速度和航向变化更新来辅助低成本INS，以增强车辆导航。光流是针对使用安装在向上看方向的消费级便携式设备（CPD）摄像头拍摄的帧计算的，以避免摄像头前方的移动物体，并利用地下停车场或隧道的典型特征，如管道和导管。另一方面，对多个质量流量传感器的测量进行建模以提供前进速度信息。此外，还提出了一种质量流量差分里程计，通过多个质量流量传感器的测量来估计车辆的航向变化。这种集成辅助系统可用于无人机（UAV）和陆地车辆导航。然而，实验结果是通过将CPD与质量流量传感器集成以辅助陆地车辆导航系统来实现的。
