Robust IMU/GPS/VO Integration for Vehicle Navigation in GNSS Degraded Urban Areas

Kim

2022

IET Radar Sonar & Navi

Efficient multi‐sensor fusion is crucial to provide accurate pose estimates for navigating various next‐generation autonomous vehicles such as self‐driving cars, personal air vehicles, urban air mobilities, and electronic vertical take‐off and landing aircraft with respect to a unified global reference frame. The integration of the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) provides globally uniform coordinate information, and the Visual‐Inertial Odometry (VIO) gives stable and accurate local pose estimates. However, the error of the integrated INS/GNSS increases rapidly in GNSS‐challenged environments, and the VIO system suffers from the long‐term error accumulation. To cope with these difficulties, this paper proposes a new tightly coupled Visual‐Inertial‐GNSS (VIG) system to achieve robust and drift‐free pose estimation utilising GNSS raw measurements, image features, and inertial measurements. A novel compressed state constraint Kalman Filter (CSCKF) is formulated to combine time‐propagated feature measurements and differential GNSS observations to aid INS. Compared to the conventional integrated navigation systems, the proposed CSCKF‐based VIG system is advantageous in maintaining the minimum number of states without unnecessary state augmentation and no loss of performance. The proposed system is evaluated by field experiments in different GNSS availability situations. The results show that accuracy is improved significantly in GNSS‐degraded environments compared to that of the conventional systems.

Section: Previous Research Studiesmentioning

confidence: 99%

A tightly‐coupled compressed‐state constraint Kalman Filter for integrated visual‐inertial‐Global Navigation Satellite System navigation in GNSS‐Degraded environments

Kim

2022

IET Radar Sonar & Navi

Mathematical Problems in Engineering

“…e computational burden of the central scheduling node is transferred to the user edge side, which greatly increases the processing efficiency and enables electric vehicles and fast charging stations to share information and synchronize processing [17]. Currently, electric vehicles can share information with fast charging stations and other systems through the Internet, upload the status and location of electric vehicles in real time, and navigate in real time based on the location of electric vehicles [18,19]. Moreover, a variety of optimal dynamic charging methods for electric fleets based on adaptive learning have been proposed, and the results show that this method can basically achieve the optimal solution.…”

Section: Fast Charging Station and Electric Vehicle System Frameworkmentioning

confidence: 99%

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

Liu¹,

Chen

Zhu

2021

The popularization of electric vehicles faces problems such as difficulty in charging, difficulty in selecting fast charging locations, and comprehensive consideration of multiple factors and vehicle interactions. With the increasingly mature application of navigation technology in vehicle-road coordination and other aspects, the proposal of an optimal dynamic charging method for electric fleets based on adaptive learning makes it possible for edge computing to process electric fleets to effectively execute the optimal route charging plan. We propose a method of electric vehicle charging service scheduling based on reinforcement learning. First, an intelligent transportation system is proposed, and on this basis a framework for the interaction between fast charging stations and electric vehicles is established. Subsequently, a dynamic travel time model for traffic sections was established. Based on the habits of electric vehicle owners, an electric vehicle charging navigation model and a reinforcement learning reward model were proposed. Finally, an electric vehicle charging navigation scheduling method is proposed to optimize the service resources of the fast charging stations in the area. The simulation results show that the method balances the charging load between stations, can effectively improve the charging efficiency of electric vehicles, and increases user satisfaction.

“…The maximum errors were 3.18, 3.51 and 2.91 m, respectively. Sun et al [25] proposed an IMU/GPS/monocular visual odometry (VO) integration method. A vehicle test was performed in a dense urban area.…”

Section: Introductionmentioning

confidence: 99%

Kalman–Hatch dual‐filter integrating global navigation satellite system/inertial navigation system/on‐board diagnostics/altimeter for precise positioning in urban canyons

Kim

2021

IET Radar Sonar & Navi

For improved positioning in urban canyons, this study proposes an efficient dual‐filter method integrating multi‐constellation global navigation satellite system (GNSS), an inertial navigation system (INS), a barometric altimeter and an on‐board diagnostics (OBD) module. The proposed method consists of a position‐domain (PD) Hatch filter and velocity Kalman filter. The Hatch filter is operated as the main positioning filter and the Kalman filter is operated as a sub‐filter to aid the main filter for the occasional GNSS outages. The Hatch filter combines barometric altimeter measurements with GNSS pseudorange and carrier phase measurements. The Kalman filter integrates the inertial measurement unit (IMU) measurements with GNSS Doppler shift and OBD speed measurements. A semi‐simulation method is applied for the accurate performance evaluation of the proposed method compared with the several conventional methods under deficient satellite visibility, multipath errors and cycle slips caused by urban canyons. By the test results, it is demonstrated that the proposed method can bound the positioning errors effectively by utilising low‐cost all‐weather sensors. The RMSEs of the proposed Kalman–Hatch dual‐filter algorithm were shown as 0.05, 0.19 and 0.11 m, respectively, and the maximum errors were shown as 0.16, 0.26 and 0.40 m, respectively, in the east, north and upward directions.