Experimental comparison of Complementary filter and Kalman filter design for low-cost sensor in quadcopter

Ngo, Ha Quang Thinh; Nguyễn, Thanh Phương; Huynh, Van Ngoc Son; Le, Thanh-Tan; Nguyen, Cong-Hung

doi:10.1109/icsse.2017.8030922

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…A DCmotor rotary actuator with PID position control drives and controls its rotational motion and an absolute encoder measures its angle of rotation. For attitude (roll and pitch tilts) measurement, the AMU utilizes a low-cost 6DoF IMU where roll and pitch are computed using IMU sensor fusion with complementary filter [26]- [27].…”

Section: B Mobile Unitmentioning

confidence: 99%

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High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

2021

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A novel, low-cost approach to high-accuracy robot localization for agricultural applications using an image-processing triple-laser-guided (TLG) system coupled with an inertial measurement unit (IMU) is presented in this paper. The TLG system consists of a laser-pointing unit (LPU) at the base station and a laser-target unit (LTU) at the mobile robot. The robot's XYZ position and heading are determined from the positions and the angles relative to the field of both LPU and LTU. The robot's roll and pitch are determined by the IMU sensor fusion with complementary filter. The IMU-coupled TLG system is demonstrated on an outdoor, 20 x 21 m flat field at various light intensities. The overall lateral and vertical accuracies of the IMU-coupled TLG system are 1.68 cm and 0.59 cm, respectively. The overall heading, roll, and pitch accuracies of the IMU-coupled TLG system are 0.90º, 0.78º, and 0.76º, respectively. The lateral and heading accuracies of the IMU-coupled TLG system are found to be comparable to commercially available GNSS-INS systems from NovAtel and Trimble, while the total cost of the IMUcoupled TLG system is only a fraction of the total cost of the commercially available localization systems.

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Section: B Mobile Unitmentioning

confidence: 99%

“…The robot's roll (  ) and pitch (  ) tilts are calculated via sensor fusion of low-frequency signals from a triple-axis accelerometer and high-frequency signals from a triple-axis gyroscope of a low-cost 6DoF IMU in the AMU through complementary filter [26]- [27].…”

Section: Determination Of Robot's Position and Orientationmentioning

confidence: 99%

High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

2021

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“…The first approach aims at studying and critically comparing the Kalman and complementary filters with respect to application in electrical vehicles. A similar study was described earlier for low‐cost sensors in quadcopter [13]. The second approach aims at improving the filter quality, based on comparison with earlier reports [17].…”

Section: Introductionmentioning

confidence: 99%

“…When low‐quality sensors are used for necessary measurement, the conventional Kalman filters are unreliable for system state estimation. This difficulty is overcome using a complementary filter to obtain high‐quality altitude extraction and gyros bias estimation [13, 14].…”

Section: Introductionmentioning

confidence: 99%

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Experimental study of a novel filter structure designed for MEMS‐based sensors in electric vehicles

Linani

Mokhtari

Cheknane

et al. 2019

IET Power Electronics

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This work describes a comparative study between Kalman filter, a complementary filter and a combination of both, for use in electrical vehicles. Combining the benefits offered by each filter to obtain an optimised filter combination is targeted. Three different combinations: The Kalman-complementary filter (KCF), complementary-Kalman filter (CKF) and 2KCFs are examined here. The filters are used to improve signals obtained via two sensors (gyroscope and accelerometer) integrated into the sensor IMU-MPU6050, with internal DMP. The sensor data are filtered to guarantee the movement quality of electrical vehicles. The KCF combination shows higher performance than the CKF combination. Moreover, the experimental results show that the 2KCF combination yields best performance with minimal noise levels and more accurate angle measurement. The optimal combination is strongly recommended for future electrical vehicle development.

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Smart Gloves Controller for Drones Using Raspberry Pi & NodeMCU

Bangera¹,

Kanojia²

2022

Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)

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Experimental comparison of Complementary filter and Kalman filter design for low-cost sensor in quadcopter

Cited by 20 publications

References 10 publications

High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

High-Accuracy Position-Aware Robot for Agricultural Automation Using Low-Cost IMU-Coupled Triple-Laser-Guided (TLG) System

Experimental study of a novel filter structure designed for MEMS‐based sensors in electric vehicles

Smart Gloves Controller for Drones Using Raspberry Pi & NodeMCU

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