Low-Cost IMU Error Intercorrection Method for Verticality Measurement

Pang, Yang; Song, Ningfang; Yang, Yanqiang; Liang, Jian; Zhang, Hao; Tian, Longjie; Liu, Zhenyue; Ma, Fu

doi:10.1109/tim.2021.3120447

Cited by 5 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the actual trajectory interval of the vehicle is relatively small, because we can determine the actual position of the car through marking and measurement. After that, we set the manual measurement error to 0.25 m [33]. From the above figure, we can see that the blue square also presents a semicircular shape and completely contains the actual track section of the vehicle.…”

Section: Determinacymentioning

confidence: 99%

Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information

et al. 2022

View full text Add to dashboard Cite

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information.

show abstract

Section: Determinacymentioning

confidence: 99%

Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information

et al. 2022

View full text Add to dashboard Cite

show abstract

“…IMU is a cost-controllable sensor that is not easily influenced by external conditions, which has been well documented in related research [9]. IMU was originally designed for ships, aircraft, and rockets which require very high accuracy while being insensitive to size and cost.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Odometry Network for Vehicle Positioning Based on Smartphone

Wang

Weng

et al. 2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Smartphone with multiple sensors integration has been widely used for navigation. The Inertial Measurement Unit (IMU) embedded in smartphones has been widely used for pedestrian navigation for counting steps. However, it is a challenge to measure the accurate velocity of the vehicle from the smartphone-embedded IMU data with a high noise level. Thus, current vehicle navigation with a smartphone relies substantially on the Global Navigation Satellite System (GNSS), which provides unreliable positions in urban dense areas due to the blockage and the reflection of GNSS signals. In this study, we propose a smartphone-based positioning method to improve vehicle positioning performance continuously in GNSS-degraded areas through the improvement of IMU velocity estimation. A convolutional neural network-Gated Recurrent Unit (CNN-GRU) combined deep learning odometry network, termed DeepOdo, is proposed to estimate the velocity of the vehicle with the IMU and barometer data as the input, rather than the traditional integral of the IMU measurements. Raw sensor data is utilized to boost the robustness. Labels of the DeepOdo are obtained from the integrated GNSS/IMU/Barometer solutions in the smartphone which significantly simplifies the dataset collection. In GNSSdenied areas, IMU, Barometer, and DeepOdo are integrated to provide accurate navigation solutions for the vehicle. Results of the proposed method show 73.14% and 98.33% improvements in horizontal and vertical directions respectively, compared with the Non-Holonomic Constraints (NHC) aided IMU. Finally, the DeepOdo network is deployed in Android smartphones to demonstrate that the proposed solution can work properly on the mobile platform.

show abstract

“…In addition, several other industrial and technological fields strongly rely on using IMUs. Few examples are verticality measurement [5],…”

Section: Introductionmentioning

confidence: 99%

Stress Testing for Performance Analysis of Orientation Estimation Algorithms

Betta

Capriglione

Carratù

et al. 2022

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Technological advancements have seen the spread of MEMS (Micro-electromechanical Systems) based sensors such as IMU (Inertial Measurement Unit) systems. The latter provide object orientation information in numerous applications such as smartphones, robotics, automotive, drones, and many others. In more detail, IMUs based on MEMS technology are characterized by small size, low power, and low cost. Technically, orientation information is provided by appropriate orientation estimation algorithms fed with quantities measured by MEMS sensors, such as acceleration, angular velocity, and magnetic field. A preliminary work pointed out that such MEMS sensors suffer significant drifts when they have to endure non-standard operating profiles in terms of thermal conditions. However, when considering very low-cost devices, the influence of temperature on the operation of orientation estimation algorithms still remains an important research gap in the literature. To deal with this important aspect, the paper proposes a specific test plan to analyze the performances of MEMS-based IMUs by considering a temperature stress test. Therefore, the effect of temperature was first analyzed on the individual sensors of the IMU (accelerometer, gyroscope, and magnetometer), then the effects of a very simple temperature compensation strategy were analyzed to highlight the importance of such step. Specifically, the effects of not compensating for temperature were studied considering a low-cost commercial IMU typically used in various fields of application. To better quantify such effects in practical applications, two wellknown orientation estimation algorithms (i.e., Complementary Filter and AHRS) typically fed by IMUs output have been considered. The experimental results obtained using the custom devices under test show that temperature compensation is necessary for the best performance of the analyzed orientation estimation algorithms.

show abstract

Low-Cost IMU Error Intercorrection Method for Verticality Measurement

Cited by 5 publications

References 22 publications

Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information

Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information

A Novel Deep Odometry Network for Vehicle Positioning Based on Smartphone

Stress Testing for Performance Analysis of Orientation Estimation Algorithms

Contact Info

Product

Resources

About