Estimate the Pitch and Heading Mounting Angles of the IMU for Land Vehicular GNSS/INS Integrated System

In this paper, we proposed a multi-sensor integrated navigation system composed of GNSS (global navigation satellite system), IMU (inertial measurement unit), odometer (ODO), and LiDAR (light detection and ranging)-SLAM (simultaneous localization and mapping). The dead reckoning results were obtained using IMU/ODO in the front-end. The graph optimization was used to fuse the GNSS position, IMU/ODO pre-integration results, and the relative position and relative attitude from LiDAR-SLAM to obtain the final navigation results in the back-end. The odometer information is introduced in the pre-integration algorithm to mitigate the large drift rate of the IMU. The sliding window method was also adopted to avoid the increasing parameter numbers of the graph optimization. Land vehicle tests were conducted in both open-sky areas and tunnel cases. The tests showed that the proposed navigation system can effectually improve accuracy and robustness of navigation. During the navigation drift evaluation of the mimic two-minute GNSS outages, compared to the conventional GNSS/INS (inertial navigation system)/ODO integration, the root mean square (RMS) of the maximum position drift errors during outages in the proposed navigation system were reduced by 62.8%, 72.3%, and 52.1%, along the north, east, and height, respectively. Moreover, the yaw error was reduced by 62.1%. Furthermore, compared to the GNSS/IMU/LiDAR-SLAM integration navigation system, the assistance of the odometer and non-holonomic constraint reduced vertical error by 72.3%. The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term. Therefore, the proposed GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM.

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“…Substituting Equation (5) into Equation (6):

where

is the IMU mounting angles, which can be obtained by the alignment calibration [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

Chang

Niu

Liu

2020

Sensors

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“…Although scarce, some literature has been produced to address the estimation of the IMU-vehicle extrinsic calibration. Most of the proposed approaches rely on the nonholonomic constraint [20][21][22][23][24], which assumes zero lateral and vertical velocity in the rear axle. However, differences are found with regard to the sensor portfolio and the quality of the sensors used, as well as the parameters identified.…”

Section: Brief Review Of Previous Approachesmentioning

confidence: 99%

“…Estimation errors of up to 2 degrees are reported, which are prohibitively large for applications requiring highly accurate motion state estimates. Some researchers have proposed methods to identify the misalignment angles between the IMU and vehicle coordinate system using IMU, odometry, GNSS information and the non-holonomic constraint [21,22]. For instance, [22] describes an approach that estimates the IMU pitch and yaw misalignment angles using a GNSS/INS aided dead reckoning approach.…”

Section: Brief Review Of Previous Approachesmentioning

confidence: 99%

“…Some researchers have proposed methods to identify the misalignment angles between the IMU and vehicle coordinate system using IMU, odometry, GNSS information and the non-holonomic constraint [21,22]. For instance, [22] describes an approach that estimates the IMU pitch and yaw misalignment angles using a GNSS/INS aided dead reckoning approach. The method is experimentally tested with data obtained from typical navigationgrade, tactical-grade and low-cost (automotive-grade) IMUs.…”

Section: Brief Review Of Previous Approachesmentioning

confidence: 99%

“…The previously presented approaches rely on the non-holonomic constraint. The main drawbacks of such methods is that when the vehicle does not comply with the nonholonomic constraint, e.g., when a side-slip angle builds during cornering or a vertical movement is caused by the suspension, errors are transferred to the misalignments estimates [22]. Furthermore, in most cases, the roll misalignment is neglected since, due to the non-holonomic constraint, it is unobservable.…”

Section: Brief Review Of Previous Approachesmentioning

confidence: 99%

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A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Marco

Kalkkuhl

Raisch

et al. 2020

Sensors

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Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.

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Estimate of Initial Installation Angle of INS in Vehicle MEMS-INS/GNSS Integrated Navigation System

Zhao

Xing

Qiu

et al. 2021

Lecture Notes in Electrical Engineering

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Estimate the Pitch and Heading Mounting Angles of the IMU for Land Vehicular GNSS/INS Integrated System

Cited by 79 publications

References 22 publications

GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Estimate of Initial Installation Angle of INS in Vehicle MEMS-INS/GNSS Integrated Navigation System

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