Design of Intelligent Mobile Robot Positioning Algorithm Based on IMU/Odometer/Lidar

Li, Zhaodong; Su, Zhibao; Yang, Tingting

doi:10.1109/sdpc.2019.00118

Cited by 17 publications

(5 citation statements)

References 1 publication

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“…In report [9], the gyroscope and accelerometer use MPU6050 components to measure the angle and angular velocity of this UAV. From the pieces of knowledge according to the above description, one can calculate the external force on the UAV by measuring the angle and angular velocity of the UAV [10,11]. It could also follow up on the IMU (inertial measurement unit system) to know the error value of this application product.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

Chen

Lin

2023

Applied Sciences

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This paper presents an analysis of a quadcopter’s stability and its sensors’ data reading using an IMU (inertial measurement unit). Firstly, the angular velocity and acceleration data are read by an Stm® development board embedded with an Stm32f401ccu6 microcontroller. The altitude of the measurement instrument platform was determined using an MS5611 barometric pressure sensor combined with a temperature sensor. The quadcopter’s control was achieved by connecting a brushless DC motor to the Stm® board, which received four PWM (pulse-width modulation) signals via the output port. An electronic governor was utilized to control the brushless DC motor, while a pre-existing remote control was designated as the transmitter. The quadcopter receiver received a 2.4 GHz signal from the transmitter using the BLE (Bluetooth low-energy) protocol, which was used to ensure the simultaneous operation of the four brushless DC motors. Finally, a PID (proportional–integral–derivative) controller was employed for parameter adjustment. The collected PID parameter program was developed in the Simulink software as a simulation platform, allowing for the execution of the Simulink model on the Stm® MCU. The Stm® module facilitates monitoring of the performance of the UAV (unmanned aerial vehicle) and enables immediate parameter adjustments to ensure flight stability. This research aims to reduce calculation errors in sensor and controller usage and improve the efficacy of the remote machine module for future industrial applications.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

Chen

Lin

2023

Applied Sciences

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“…Fusion indoor localization methods are presented to eliminate the above shortcomings. The widely installed indoor surveillance cameras are combined with smartphone IMU data to achieve indoor localization with high accuracy [25][26][27] . Furthermore, WiFi and PDR integrated with an extended Kalman filter are presented to achieve high-ranking accuracy [28] .…”

Section: Introductionmentioning

confidence: 99%

From Coarse to Fine: Two-Stage Indoor Localization with Multisensor Fusion

Zhang

Bao

et al. 2023

Tsinghua Sci. Technol.

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Increasing attention has been paid to high-precision indoor localization in dense urban and indoor environments. Previous studies have shown single indoor localization methods based on WiFi fingerprints, surveillance cameras or Pedestrian Dead Reckoning (PDR) are restricted by low accuracy, limited tracking region, and accumulative error, etc., and some defects can be resolved with more labor costs or special scenes. However, requesting more additional information and extra user constraints is costly and rarely applicable. In this paper, a two-stage indoor localization system is presented, integrating WiFi fingerprints, the vision of surveillance cameras, and PDR (the system abbreviated as iWVP). A coarse location using WiFi fingerprints is done advanced, and then an accurate location by fusing data from surveillance cameras and the IMU sensors is obtained. iWVP uses a matching algorithm based on motion sequences to confirm the identity of pedestrians, enhancing output accuracy and avoiding corresponding drawbacks of each subsystem. The experimental results show that the iWVP achieves high accuracy with an average position error of 4.61 cm, which can effectively track pedestrians in multiple regions in complex and dynamic indoor environments.

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“…Inertial Measurement Unit (IMU) sensors are an essential component in various applications involving movement and orientation monitoring, their implementation is mostly for unmanned aircraft [1][5] [12][13], augmented reality [2][3] [11 ] [14], robotics [4] [7][8] [9], and autonomous vehicles [6], However, the data provided by IMU sensors tends to be polluted by interference and noise, resulting in less accurate estimated values. In this context, the Kalman Filter algorithm is an effective solution to improve the quality of IMU sensor data and make more reliable predictions [10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter

Mobed Bachtiar,

Wibowo,

Rifa’I

et al. 2023

Int. J. Artif. Intell. Robot.

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The Inertial Measurement Unit (IMU) sensor is a tool used to measure the speed and acceleration of an object in 3 dimensions (x, y, z). IMU sensors are often used in robotics, drone control, autonomous vehicles, and augmented reality applications. Usually, the data obtained from the IMU sensor is contaminated by interference and noise, which can reduce measurement accuracy. Kalman Filter is a statistical method used to combine measurement data with a mathematical system model to produce better estimates. In the IMU context, the Kalman Filter removed interference and noise affecting acceleration and speed data so that IMU sensor data could be estimated more accurately. This algorithm predicts the next data state based on previous data and updates the prediction with new measurement data. The measurement implementation in this research is the IMU sensor on the GY-91 module to determine the object's tilt on the pitch, roll, and yaw axes during flight. The ARM STM32F407VGT6 microcontroller pin reads the sensor, and then the estimation and prediction process is carried out using the Kalman filter algorithm. With the parameters Kalman Measurement Error = 1, Estimation Error = 0.12, and Covariance Process = 0.4, it can predict the reading results from the IMU sensor well.

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Design of Intelligent Mobile Robot Positioning Algorithm Based on IMU/Odometer/Lidar

Cited by 17 publications

References 1 publication

Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

From Coarse to Fine: Two-Stage Indoor Localization with Multisensor Fusion

Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter

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