Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Intelligent Driving Function Department, IAV GmbH, 10587 Berlin, Germany.
Sensors (Basel). 2019 Nov 26;19(23):5178. doi: 10.3390/s19235178.
Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.
本地化是智能车辆的基本问题。对于车辆自主运行，首先需要在环境中定位自己。多年来，已经引入了许多不同的里程计（视觉、惯性、车轮编码器）用于自主车辆定位。然而，由于依赖于传感器测量的集成，这种里程计会出现漂移。在本文中，我们对里程计中的漂移误差进行了建模，并引入了漂移协方差估计（DCE）算法。DCE 算法使用不受漂移影响的另一个车载传感器的读数来估计里程计的协方差。为了验证所提出的算法，在不同条件下进行了多次实际实验，以及来自牛津机器人车数据集和欧盟长期驾驶数据集的序列。与使用恒定协方差相比，研究了协方差估计对三种不同的基于融合的定位算法（EKF、UKF 和 EH-无穷大）的影响，恒定协方差是根据所使用传感器的真实方差计算得出的。所得结果表明，与恒定协方差相比，估计算法在提高定位精度方面的有效性。
