Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System

Fu, Qing; Li, Sihai; Liu, Y.; Zhou, Qi; Wu, Feng

doi:10.3390/s18124230

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Generally, when the wheel angle measurement system is actually installed, it is difficult for the RTK-GNSS antenna and MEMS1 to coincide with the center of mass of the agricultural machinery. As shown in Figure 2, this introduces a systematic measurement error [32,33] .…”

Section: Lever Arm Effect Compensationmentioning

confidence: 99%

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

Wang¹,

Hu²,

He³

et al. 2022

International Journal of Agricultural and Biological Engineering

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Aiming at the application environment of paddy agricultural machinery with bumpy and undulating changes, the problems affecting the method for steering wheel angle measurement by MEMS gyroscope were analyzed, and a wheel angle measurement method combining Dual-MEMS gyroscope (dual MEMS gyroscope) and RTK-GNSS was designed. The adaptive weighting method was used to fuse the heading angle differentiation of RTK-GNSS, the MEMS gyroscope angle rate, and velocity data, and the rod-arm compensation was performed to accurately obtain the angle rates of the body and steering wheels of agricultural machinery; the difference between the combined angular rate of the steering wheel of the agricultural machinery and the angular rate of the agricultural machinery body was obtained, and the integrator is used to integrate the difference to get the wheel steering angle value, and the Kalman filter was designed to make feedback correction for the integration process of angle calculation to eliminate the errors caused by the gyroscope zero bias, random drift, and gyroscope rod arm effect, and to obtain the accurate value of wheel steering angle. A comparative test with the connecting rod wheel angle sensor was designed, and the results show that the maximum deviation is 4.99°, the average absolute average value is 1.61°, and the average standard deviation is 0.98°. The method in this study and the connecting rod wheel angle sensor were used on paddy farm machinery. The wheel angle measurement deviation of the proposed method and the connecting rod wheel angle sensor was not more than 1°, which is relatively small. It has good stability, speed adaptability, and dynamic responsiveness that meets the accuracy requirements of steering wheel angle measurement for paddy field agricultural machinery unmanned driving and can be used instead of connecting rod angle sensors for unmanned agricultural machinery.

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Section: Lever Arm Effect Compensationmentioning

confidence: 99%

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

Wang¹,

Hu²,

He³

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…Robust orientation estimation is of great significance in robotics tasks such as motion control, autonomous navigation, and 3D mapping. Orientation can be obtained by utilizing carried sensors like the wheel encoder, inertial measurement unit (IMU) [1][2][3][4], or cameras [5][6][7]. Among these solutions, the visual-based method [8][9][10][11] is effective, as cameras can conveniently capture informative images to estimate orientation and position.…”

Section: Introductionmentioning

confidence: 99%

Robust Visual Compass Using Hybrid Features for Indoor Environments

et al. 2019

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Orientation estimation is a crucial part of robotics tasks such as motion control, autonomous navigation, and 3D mapping. In this paper, we propose a robust visual-based method to estimate robots’ drift-free orientation with RGB-D cameras. First, we detect and track hybrid features (i.e., plane, line, and point) from color and depth images, which provides reliable constraints even in uncharacteristic environments with low texture or no consistent lines. Then, we construct a cost function based on these features and, by minimizing this function, we obtain the accurate rotation matrix of each captured frame with respect to its reference keyframe. Furthermore, we present a vanishing direction-estimation method to extract the Manhattan World (MW) axes; by aligning the current MW axes with the global MW axes, we refine the aforementioned rotation matrix of each keyframe and achieve drift-free orientation. Experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for orientation estimation. In addition, we have applied our proposed visual compass to pose estimation, and the evaluation on public sequences shows improved accuracy.

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“…Robust pose estimation and environment mapping are of great significance in the execution of robotic tasks, such as motion control and navigation. The robot’s pose and the scene’s map can be obtained by utilizing robotic sensors, such as wheel encoders, inertial measurement units [2,3,4], lasers [5,6], and cameras [7,8,9]. Among these solutions, the visual-based method is one of the more effective approaches because cameras can conveniently capture informative images to estimate the robot’s poses and perceive its surroundings.…”

Section: Introductionmentioning

confidence: 99%

RGB-D SLAM Using Point–Plane Constraints for Indoor Environments

Guo

Peng

Wang

et al. 2019

Sensors

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Pose estimation and map reconstruction are basic requirements for robotic autonomous behavior. In this paper, we propose a point–plane-based method to simultaneously estimate the robot’s poses and reconstruct the current environment’s map using RGB-D cameras. First, we detect and track the point and plane features from color and depth images, and reliable constraints are obtained, even for low-texture scenes. Then, we construct cost functions from these features, and we utilize the plane’s minimal representation to minimize these functions for pose estimation and local map optimization. Furthermore, we extract the Manhattan World (MW) axes on the basis of the plane normals and vanishing directions of parallel lines for the MW scenes, and we add the MW constraint to the point–plane-based cost functions for more accurate pose estimation. The results of experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for pose estimation and map reconstruction, and we show its advantages compared with alternative methods.

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Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System

Cited by 7 publications

References 20 publications

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

Robust Visual Compass Using Hybrid Features for Indoor Environments

RGB-D SLAM Using Point–Plane Constraints for Indoor Environments

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