High Accuracy Three-Dimensional Self-Localization using Visual Markers and Inertia Measurement Unit

AbstractIn this paper, we introduce the multi-variate spectral quasi-linearization method which is an extension of the previously reported bivariate spectral quasi-linearization method. The method is a combination of quasi-linearization techniques and the spectral collocation method to solve three-dimensional partial differential equations. We test its applicability on the (2 + 1) dimensional Burgers’ equations. We apply the spectral collocation method to discretize both space variables as well as the time variable. This results in high accuracy in both space and time. Numerical results are compared with known exact solutions as well as results from other papers to confirm the accuracy and efficiency of the method. The results show that the method produces highly accurate solutions and is very efficient for (2 + 1) dimensional PDEs. The efficiency is due to the fact that only few grid points are required to archive high accuracy. The results are portrayed in tables and graphs.

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Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417732757 ◽

2017 ◽

Vol 14 (5) ◽

pp. 172988141773275 ◽

Cited By ~ 2

Author(s):

Francisco J Perez-Grau ◽

Fernando Caballero ◽

Antidio Viguria ◽

Anibal Ollero

Keyword(s):

Monte Carlo ◽

Motion Tracking ◽

Robot Navigation ◽

Tracking System ◽

Three Dimensional ◽

Measurement Unit ◽

Indoor Environments ◽

Localization Algorithm ◽

Monte Carlo Localization ◽

Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

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A High-Accuracy Mechanical Quadrature Method for Solving the Axisymmetric Poisson's Equation

Advances in Applied Mathematics and Mechanics ◽

10.4208/aamm.2015.m1287 ◽

2017 ◽

Vol 9 (2) ◽

pp. 393-406 ◽

Cited By ~ 4

Author(s):

Hu Li ◽

Jin Huang

Keyword(s):

Three Dimensional ◽

High Accuracy ◽

Poisson’S Equation ◽

Two Dimensional ◽

Quadrature Method ◽

Poisson's Equation ◽

Mechanical Quadrature ◽

Asymptotic Error ◽

Accuracy Order ◽

Mechanical Quadrature Method

AbstractIn this article, we consider the numerical solution for Poisson's equation in axisymmetric geometry. When the boundary condition and source term are axisymmetric, the problem reduces to solving Poisson's equation in cylindrical coordinates in the two-dimensional (r,z) region of the original three-dimensional domain S. Hence, the original boundary value problem is reduced to a two-dimensional one. To make use of the Mechanical quadrature method (MQM), it is necessary to calculate a particular solution, which can be subtracted off, so that MQM can be used to solve the resulting Laplace problem, which possesses high accuracy order and low computing complexities. Moreover, the multivariate asymptotic error expansion of MQM accompanied with for all mesh widths hi is got. Hence, once discrete equations with coarse meshes are solved in parallel, the higher accuracy order of numerical approximations can be at least by the splitting extrapolation algorithm (SEA). Meanwhile, a posteriori asymptotic error estimate is derived, which can be used to construct self-adaptive algorithms. The numerical examples support our theoretical analysis.

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Investigation into Hand Scraping: A Microanalysis

Journal of Manufacturing and Materials Processing ◽

10.3390/jmmp2040076 ◽

2018 ◽

Vol 2 (4) ◽

pp. 76 ◽

Cited By ~ 1

Author(s):

Kai Oßwald ◽

Ingo Lochmahr ◽

Yasin Bagci ◽

Peter Saile

Keyword(s):

Inertial Measurement Unit ◽

Three Dimensional ◽

Movement Pattern ◽

Measurement Unit ◽

Surface Finishing ◽

Future Research ◽

Tool Orientation ◽

Inertial Measurement ◽

Finishing Process ◽

Basic Characteristics

Hand scraping is a manual surface finishing process that, despite its low productivity and high cost, is still applied in many industries because of its advantages concerning accuracy and tribology. In the presented microanalysis forces, movement patterns and tool orientation of individual hand scraping strokes were measured using a test stand, specifically designed for this purpose. It utilizes a camera, a three dimensional dynamometer, and an inertial measurement unit (IMU). The results show the basic characteristics of hand scraping. Typical courses of relevant quantities like cutting force, passive force, clearance, and directional angle are shown. In addition, the movement pattern of the tool during individual scraping strokes is analyzed. This research aims to contribute to a later implementation of automated scraping. The conducted research creates a base for future research regarding different scraping methods and achieved results.

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Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

JMIR Biomedical Engineering ◽

10.2196/21105 ◽

2021 ◽

Vol 6 (1) ◽

pp. e21105

Author(s):

Arpita Mallikarjuna Kappattanavar ◽

Nico Steckhan ◽

Jan Philipp Sachs ◽

Harry Freitas da Cruz ◽

Erwin Böttinger ◽

...

Keyword(s):

Three Dimensional ◽

Pressure Sensors ◽

Ground Truth ◽

Living Environment ◽

Measurement Unit ◽

Future Research ◽

Free Living ◽

Medium Quality ◽

Underlying Causes ◽

Future Work

Background A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements. Methods A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.

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USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-433-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 433-439 ◽

Cited By ~ 4

Author(s):

Bisheng Yang ◽

Yuan Liu ◽

Fuxun Liang ◽

Zhen Dong

Keyword(s):

Laser Scanning ◽

Human Error ◽

Traffic Accidents ◽

Three Dimensional ◽

Point Clouds ◽

High Accuracy ◽

3D Point Clouds ◽

Assistant Systems ◽

Novel Method ◽

High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

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