scholarly journals Micro air vehicle local pose estimation with a two-dimensional laser scanner: A case study for electric tower inspection

2017 ◽  
Vol 10 (2) ◽  
pp. 127-156
Author(s):  
Carlos Viña ◽  
Pascal Morin
Keyword(s):  
Inertial Measurement Unit ◽  
Position Control ◽  
Micro Air Vehicles ◽  
Indoor Navigation ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Two Dimensional ◽  
Inertial Measurement ◽  
Inspection Tasks ◽  
Air Vehicles

Automation of inspection tasks is crucial for the development of the power industry, where micro air vehicles have shown a great potential. Self-localization in this context remains a key issue and is the main subject of this work. This article presents a methodology to obtain complete three-dimensional local pose estimates in electric tower inspection tasks with micro air vehicles, using an on-board sensor set-up consisting of a two-dimensional light detection and ranging, a barometer sensor and an inertial measurement unit. First, we present a method to track the tower’s cross-sections in the laser scans and give insights on how this can be used to model electric towers. Then, we show how the popular iterative closest point algorithm, that is typically limited to indoor navigation, can be adapted to this scenario and propose two different implementations to retrieve pose information. This is complemented with attitude estimates from the inertial measurement unit measurements, based on a gain-scheduled non-linear observer formulation. An altitude observer to compensate for barometer drift is also presented. Finally, we address velocity estimation with views to feedback position control. Validations based on simulations and experimental data are presented.

scholarly journals Valid and Reliable Barbell Velocity Estimation Using an Inertial Measurement Unit

2021 ◽  
Vol 18 (17) ◽  
pp. 9170
Author(s):  
Steffen Held ◽  
Ludwig Rappelt ◽  
Jan-Philip Deutsch ◽  
Lars Donath
Keyword(s):  
Inertial Measurement Unit ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Systematic Bias ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
Intraclass Correlation Coefficients ◽  
Reliability Indicators

The accurate assessment of the mean concentric barbell velocity (MCV) and its displacement are crucial aspects of resistance training. Therefore, the validity and reliability indicators of an easy-to-use inertial measurement unit (VmaxPro®) were examined. Nineteen trained males (23.1 ± 3.2 years, 1.78 ± 0.08 m, 75.8 ± 9.8 kg; Squat 1-Repetition maximum (1RM): 114.8 ± 24.5 kg) performed squats and hip thrusts (3–5 sets, 30 repetitions total, 75% 1RM) on two separate days. The MCV and displacement were simultaneously measured using VmaxPro® and a linear position transducer (Speed4Lift®). Good to excellent intraclass correlation coefficients (0.91 < ICC < 0.96) with a small systematic bias (p < 0.001; ηp2 < 0.50) for squats (0.01 ± 0.04 m·s−1) and hip thrusts (0.01 ± 0.05 m·s−1) and a low limit of agreement (LoA < 0.12 m·s−1) indicated an acceptable validity. The within- and between-day reliability of the MCV revealed good ICCs (0.55 < ICC < 0.91) and a low LoA (<0.16 m·s−1). Although the displacement revealed a systematic bias during squats (p < 0.001; ηp2 < 0.10; 3.4 ± 3.4 cm), no bias was detectable during hip thrusts (p = 0.784; ηp2 < 0.001; 0.3 ± 3.3 cm). The displacement showed moderate to good ICCs (0.43 to 0.95) but a high LoA (7.8 to 10.7 cm) for the validity and (within- and between-day) reliability of squats and hip thrusts. The VmaxPro® is considered to be a valid and reliable tool for the MCV assessment.

scholarly journals Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3946 ◽  
Author(s):  
Faisal Jamil ◽  
Do Hyeun Kim
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Indoor Navigation ◽  
Prediction Algorithm ◽  
Measurement Unit ◽  
Sensor Reading ◽  
Inertial Measurement ◽  
Learning Module ◽  
Alpha Beta

The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha–beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of RMSE.

scholarly journals Globally stable velocity estimation using normalized velocity measurement

2019 ◽  
Vol 39 (1) ◽  
pp. 143-157
Author(s):  
Elias Bjørne ◽  
Edmund F Brekke ◽  
Torleiv H Bryne ◽  
Jeff Delaune ◽  
Tor Arne Johansen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Velocity Estimation ◽  
Nonlinear Observer ◽  
Measurement Unit ◽  
Velocity Measurements ◽  
Nonlinear Methods ◽  
Inertial Measurement ◽  
Globally Stable

The problem of estimating velocity from a monocular camera and calibrated inertial measurement unit (IMU) measurements is revisited. For the presented setup, it is assumed that normalized velocity measurements are available from the camera. By applying results from nonlinear observer theory, we present velocity estimators with proven global stability under defined conditions, and without the need to observe features from several camera frames. Several nonlinear methods are compared with each other, also against an extended Kalman filter (EKF), where the robustness of the nonlinear methods compared with the EKF are demonstrated in simulations and experiments.

A Low-Cost, Redundant Inertial Measurement Unit for Unmanned Air Vehicles

2000 ◽  
Vol 19 (11) ◽  
pp. 1089-1103 ◽  
Author(s):  
Salah Sukkarieh ◽  
Peter Gibbens ◽  
Ben Grocholsky ◽  
Keith Willis ◽  
Hugh F. Durrant-Whyte
Keyword(s):  
Inertial Measurement Unit ◽  
Low Cost ◽  
Measurement Unit ◽  
Unmanned Air Vehicles ◽  
Inertial Measurement ◽  
Air Vehicles

Velocity Estimation for Quadrupeds Based on Extended Kalman Filter

2014 ◽  
Vol 621 ◽  
pp. 525-532 ◽  
Author(s):  
Man Tian Li ◽  
Cong Wei Wang ◽  
Peng Fei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Random Error ◽  
Inertial Measurement Unit ◽  
Quadruped Robot ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Forward Kinematics ◽  
Random Errors ◽  
Inertial Measurement

Measuring robots’ real-time velocity correctly is important for locomotion control. Inertial Measurement Unit (IMU) is widely used for velocity measurement. Limited by the bias and random error, IMU alone often can’t meet the requirement. This paper makes use of Extended Kalman Filter (EKF) to fuse kinematics and IMU, and inhibits the drift successfully. We calibrate the bias and recognize the random errors of IMU. Then the forward kinematics of legs is established and the EKF algorithm for velocity estimation is designed based on IMU and kinematics. Finally, the presented algorithm is validated in simulation and on a quadruped robot based on hydraulic driver in trotting gait.

scholarly journals Front-Crawl Instantaneous Velocity Estimation Using a Wearable Inertial Measurement Unit

Sensors ◽  
2012 ◽  
Vol 12 (10) ◽  
pp. 12927-12939 ◽  
Author(s):  
Farzin Dadashi ◽  
Florent Crettenand ◽  
Grégoire P. Millet ◽  
Kamiar Aminian
Keyword(s):  
Inertial Measurement Unit ◽  
Velocity Estimation ◽  
Instantaneous Velocity ◽  
Measurement Unit ◽  
Front Crawl ◽  
Inertial Measurement
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