Inertial Navigation System Based Vehicle Temporal Relative Localization With Split Covariance Intersection Filter

In autonomous vehicle navigation, it usually involves a basic process of estimating the vehicle pose at one instant relative to the vehicle pose at a previous instant, and we refer to this process as temporal relative localization (TRL). Accurate and reliable TRL is valuable for vehicle localization and mapping which plays a fundamental role in autonomous vehicle navigation. A common way for realizing TRL is dead reckoning based on motion sensor such as inertial navigation system (INS). To augment the performance of INS based TRL, a nine-degree-of-freedom (9-DOF) vehicle dynamics model (VDM) is proposed as the system model for the filtering process of TRL; it can effectively characterize vehicle pitch and roll motion, especially in slope road scenarios. For better fault tolerance, a distributed fusion framework which consists of a master filter and two local filters for multi-source data fusion is introduced. The split covariance intersection filter (Split CIF) is applied to handle unknown temporal and spatial correlation that inevitably exists in such distributed fusion mechanism. A comparative experimental study demonstrates the advantage of the proposed method in terms of accuracy and robustness.

I. Introduction

In Recent years, autonomous driving has become an important research topic with the support of rapidly developing computer technology, robotics and sensor technology, etc [1]. As a fundamental function of autonomous vehicle, localization [2], [3] aims to accurately and reliably estimate the vehicle state, which often involves a basic process of estimating the vehicle pose at one instant relative to previous moment, and we refer as temporal relative localization (TRL). The common way to realize TRL is applying motion sensor based dead reckoning, such as odometer based methods [4], inertial navigation system based methods [5]–[7] and other methods [8], [9]. For autonomous vehicle localization, inertial measurement unit (IMU) is a commonly-adopted motion sensor, which further possesses the merit of high instantaneous measurement accuracy. In practice, the inertial navigation system (INS) is usually not used alone but integrated with some motion sensors or systems [10], [11].