Abstract
Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.
Highlights
Automated driving techniques are widely admitted as a promising and challenging way to avoid road crashes and improve traffic conditions [1]
A robust localization solution can be achieved by blending GNSS, INS and DMI (Distance Measuring Instruments) techniques in a way that utilizes the strengths of each individual system and mitigates their weaknesses
Sensors 2017, 17, 2140 an INS/GPS sensor fusion scheme based on the State-Dependent Riccati Equation (SDRE) nonlinear filtering method is proposed for Unmanned Aerial Vehicles (UAV), which is widely used in the optimal nonlinear control and filtering literature
Summary
Automated driving techniques are widely admitted as a promising and challenging way to avoid road crashes and improve traffic conditions [1]. Sensors 2017, 17, 2140 an INS/GPS sensor fusion scheme based on the State-Dependent Riccati Equation (SDRE) nonlinear filtering method is proposed for Unmanned Aerial Vehicles (UAV), which is widely used in the optimal nonlinear control and filtering literature. UKF has been proven to be a promising method for GPS/INS fusion, the accuracy and reliability performance still need to be improved for autonomous vehicles under urban environments. To further improve the accuracy and reliability of localization for autonomous vehicles in urban environments, we firstly propose a fault-detection-based loosely-coupled.
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