A SINS/GNSS/2D-LDV integrated navigation scheme for unmanned ground vehicles

Abstract

In recent years, unmanned autonomous driving technology has attracted increasing attention from people, and become a research hotspot. Currently, the integration of strapdown inertial navigation system (SINS) and global navigation satellite systems (GNSSs) is the most common and effective navigation and positioning scheme for unmanned ground vehicles (UGVs) and unmanned aerial vehicles. However, this integrated system is unable to maintain a reliable positioning solution in challenging environments due to the inherent weakness of GNSS signals and the accumulation of SINS positioning errors over time. To address this issue, this paper proposes an integrated scheme based on an asynchronous Kalman filter for SINS, GNSS and two-dimensional (2D) laser Doppler velocimeter (LDV). In the proposed scheme, the SINS and 2D-LDV are tightly coupled to improve the robustness of the integrated system, and the error parameters between the 2D-LDV and the SINS are calibrated in real time during the validity of the GNSS signal. In addition, the designed asynchronous Kalman filter method evaluates the validity of the GNSS and 2D-LDV measurements in real time based on Mahalanobis distance of innovation vector and statistical property principle. Two groups of long-distance, high-mobility vehicle experiments conducted in challenging environments verify the validity of the proposed scheme. The experimental results show that the proposed SINS/GNSS/2D-LDV integrated navigation scheme has good environmental adaptability and reliability, and can maintain high horizontal and vertical positioning accuracy despite frequent GNSS signal failures, which can meet the needs of UGVs.

Online calibration method for SINS/LDV integrated navigation system based on left group error definition

“…The integration of strapdown inertial navigation system (SINS) with laser Doppler velocimeter (LDV) is a promising scheme for land vehicles that require high positioning accuracy [1][2][3][4]. In SINS/LDV integrated navigation system, the SINS is the core navigation equipment, with * Author to whom any correspondence should be addressed.…”

“…Through this model, the consistency of filtering can be well guaranteed. Chang et al [33] shows that by introducing an auxiliary velocity, the mechanization of SINS in the earth-centered earth-fixed (ECEF) frame satisfies the 'group affine' property with group state on SE 3 (3). The obtained left and rightinvariant error state models are trajectory-independent, and their log-linearity, an advantage, makes them very suitable for attitude initialization with arbitrary misalignment.…”

“…Tang et al [34] shows that for SINS/DVL integration using the error state model proposed in [33], the left-invariant error definition is more suitable for SINS/DVL navigation when considering IMU bias. Chang et al [35] incorporates the DR position of the SINS/DVL integration with the attitude, velocity, and position computed by SINS as the elements of the group of triple direct isometries SE 3 (3). Based on the left-invariant error definition, it derives the corresponding error state model.…”

Adaptive Kalman filter with LSTM network assistance for abnormal measurements

“…Kalman filter has been widely used in various fields such as autonomous driving [1,2], human pose estimation [3,4], and environmental monitoring [5,6]. While Kalman filtering excels in handling Gaussian noises, its adaptability to non-Gaussian noise is limited in specific practical applications.…”

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

“…For , as time accumulates, cumulative errors will occur. The longer the time, the greater the cumulative error, which requires timely correction by other navigation equipment [ 6 , 7 , 8 ].…”