Research on GNSS INS &amp; GNSS/INS Integrated Navigation Method for Autonomous Vehicles: A Survey

He, Yuan; Li, Jichuan; Liu, Junjie

doi:10.1109/access.2023.3299290

Cited by 11 publications

(4 citation statements)

References 110 publications

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“…Constructing the mathematical model of GNSS/INS fusion navigation using the Kalman filter tightly coupled algorithm as an example [24].…”

Section: Mathematical Model Of Gnss/ins Fusion Navigationmentioning

confidence: 99%

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

Chen,

Zhang,

You

et al. 2024

Applied Sciences

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In response to the challenge of single navigation methods failing to meet the high precision requirements for unmanned aerial vehicle (UAV) navigation in complex environments, a novel algorithm that integrates Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) navigation information is proposed to enhance the positioning accuracy and robustness of UAV navigation systems. First, the fundamental principles of Kalman filtering and its application in navigation are introduced. Second, the basic principles of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and their applications in the navigation domain are elaborated. Subsequently, an algorithm based on a CNN and LSTM-assisted Kalman filtering fusion navigation is proposed. Finally, the feasibility and effectiveness of the proposed algorithm are validated through experiments. Experimental results demonstrate that the Kalman filtering fusion navigation algorithm assisted by a CNN and LSTM significantly improves the positioning accuracy and robustness of UAV navigation systems in highly interfered complex environments.

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“…Constructing the mathematical model of GNSS/INS fusion navigation using the Kalman filter tightly coupled algorithm as an example [24].…”

Section: Mathematical Model Of Gnss/ins Fusion Navigationmentioning

confidence: 99%

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

Chen,

Zhang,

You

et al. 2024

Applied Sciences

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“…In the outdoor environment, although the DGNSS can provide more accurate positioning information in most cases, considering the difficulty of promoting the application of orchard smart mowers, the cost of the sensor and its subsequent use is a problem that cannot be ignored, considering the difficulty of promoting the application of smart mowers in orchards. The high cost of GNSS differential services increases the cost of lawn mower navigation operations; therefore, when an existing positioning system can achieve high-precision positioning, it should attempt to turn off the differential service to reduce the cost, and a single GNSS module positioning frequency that is low in orchard environments is also susceptible to signal obstruction, unstable communication delays, ground reference station distance limitations, and other issues affecting the accuracy of GNSS positioning; therefore, it often needs to be combined with other sensors for positioning [3][4][5][6]. Mower chassis motors are generally equipped with encoders, so the mower positioning accuracy is often improved by constructing a mower odometry algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on a Low-Cost High-Precision Positioning System for Orchard Mowers

Fei,

Mai,

Jiang

et al. 2024

Agriculture

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To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards.

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“…He and Liu, in [30], compared four integrated hierarchical satellite and inertial navigation algorithms for motion control and positioning of autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

Radar Perception of Multi-Object Collision Risk Neural Domains during Autonomous Driving

Lisowski

2024

Electronics

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The analysis of the state of the literature in the field of methods of perception and control of the movement of autonomous vehicles shows the possibilities of improving them by using an artificial neural network to generate domains of prohibited maneuvers of passing objects, contributing to increasing the safety of autonomous driving in various real conditions of the surrounding environment. This article concerns radar perception, which involves receiving information about the movement of many autonomous objects, then identifying and assigning them a collision risk and preparing a maneuvering response. In the identification process, each object is assigned a domain generated by a previously trained neural network. The size of the domain is proportional to the risk of collisions and distance changes during autonomous driving. Then, an optimal trajectory is determined from among the possible safe paths, ensuring control in a minimum of time. The presented solution to the radar perception task was illustrated with a computer simulation of autonomous driving in a situation of passing many objects. The main achievements presented in this article are the synthesis of a radar perception algorithm mapping the neural domains of autonomous objects characterizing their collision risk and the assessment of the degree of radar perception on the example of multi-object autonomous driving simulation.

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Research on GNSS INS & GNSS/INS Integrated Navigation Method for Autonomous Vehicles: A Survey

Cited by 11 publications

References 110 publications

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network

Research on a Low-Cost High-Precision Positioning System for Orchard Mowers

Radar Perception of Multi-Object Collision Risk Neural Domains during Autonomous Driving

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