Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

Development of low-cost mobile laser scanning for 3D construction indoor mapping by using inertial measurement unit, ultra-wide band and 2D laser scanner

2019 ◽  
Vol 26 (7) ◽  
pp. 1367-1386
Author(s):  
Chao Chen ◽  
Llewellyn Tang ◽  
Craig Matthew Hancock ◽  
Penghe Zhang
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
Laser Scanning ◽  
Low Cost ◽  
Laser Scanner ◽  
Wide Band ◽  
Measurement Unit ◽  
Content Type ◽  
Inertial Measurement ◽  
Mls Method

Purpose The purpose of this paper is to introduce the development of an innovative mobile laser scanning (MLS) method for 3D indoor mapping. The generally accepted and used procedure for this type of mapping is usually performed using static terrestrial laser scanning (TLS) which is high-cost and time-consuming. Compared with conventional TLS, the developed method proposes a new idea with advantages of low-cost, high mobility and time saving on the implementation of a 3D indoor mapping. Design/methodology/approach This method integrates a low-cost 2D laser scanner with two indoor positioning techniques – ultra-wide band (UWB) and an inertial measurement unit (IMU), to implement a 3D MLS for reality captures from an experimental indoor environment through developed programming algorithms. In addition, a reference experiment by using conventional TLS was also conducted under the same conditions for scan result comparison to validate the feasibility of the developed method. Findings The findings include: preset UWB system integrated with a low-cost IMU can provide a reliable positioning method for indoor environment; scan results from a portable 2D laser scanner integrated with a motion trajectory from the IMU/UWB positioning approach is able to generate a 3D point cloud based in an indoor environment; and the limitations on hardware, accuracy, automation and the positioning approach are also summarized in this study. Research limitations/implications As the main advantage of the developed method is low-cost, it may limit the automation of the method due to the consideration of the cost control. Robotic carriers and higher-performance 2D laser scanners can be applied to realize panoramic and higher-quality scan results for improvements of the method. Practical implications Moreover, during the practical application, the UWB system can be disturbed by variances of the indoor environment, which can affect the positioning accuracy in practice. More advanced algorithms are also needed to optimize the automatic data processing for reducing errors caused by manual operations. Originality/value The development of this MLS method provides a novel idea that integrates data from heterogeneous systems or sensors to realize a practical aim of indoor mapping, and meanwhile promote the current laser scanning technology to a lower-cost, more flexible, more portable and less time-consuming trend.

Maximum Correntropy Kalman Filter for Orientation Estimation With Application to LiDAR Inertial Odometry

2020 ◽  
Author(s):  
Seyed Fakoorian ◽  
Matteo Palieri ◽  
Angel Santamaria-Navarro ◽  
Cataldo Guaragnella ◽  
Dan Simon ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Attitude Estimation ◽  
High Rate ◽  
Measurement Unit ◽  
Orientation Estimation ◽  
Localization Accuracy ◽  
Inertial Measurement ◽  
Robotic Applications

Abstract Accurate attitude estimation using low-cost sensors is an important capability to enable many robotic applications. In this paper, we present a method based on the concept of correntropy in Kalman filtering to estimate the 3D orientation of a rigid body using a low-cost inertial measurement unit (IMU). We then leverage the proposed attitude estimation framework to develop a LiDAR-Intertial Odometry (LIO) demonstrating improved localization accuracy with respect to traditional methods. This is of particular importance when the robot undergoes high-rate motions that typically exacerbate the issues associated with low-cost sensors. The proposed orientation estimation approach is first validated using the data coming from a low-cost IMU sensor. We further demonstrate the performance of the proposed LIO solution in a simulated robotic cave exploration scenario.

An inertial measurement unit tracking system for body movement in comparison with optical tracking

2020 ◽  
Vol 234 (7) ◽  
pp. 728-737
Author(s):  
Rui Li ◽  
Barclay Jumet ◽  
Hongliang Ren ◽  
WenZhan Song ◽  
Zion Tsz Ho Tse
Keyword(s):  
Inertial Measurement Unit ◽  
Tracking System ◽  
Low Cost ◽  
Optical Tracking ◽  
Measurement Unit ◽  
Joint Movement ◽  
Joint Angles ◽  
Optical Tracking System ◽  
Microelectromechanical System ◽  
Inertial Measurement

The recent advancement of motion tracking technology offers better treatment tools for conditions, such as movement disorders, as the outcome of the rehabilitation could be quantitatively defined. The accurate and fast angular information output of the inertial measurement unit tracking systems enables the collection of accurate kinematic data for clinical assessment. This article presents a study of a low-cost microelectromechanical system inertial measurement unit-based tracking system in comparison with the conventional optical tracking system. The system consists of seven microelectromechanical system inertial measurement units, which could be mounted on the lower limbs of the subjects. For the feasibility test, 10 human participants were instructed to perform three different motions: walking, running, and fencing lunges when wearing specially designed sleeves. The subjects’ lower body movements were tracked using our inertial measurement unit-based system and compared with the gold standard—the NDI Polaris Vega optical tracking system. The results of the angular comparison between the inertial measurement unit and the NDI Polaris Vega optical tracking system were as follows: the average cross-correlation value was 0.85, the mean difference of joint angles was 2.00°, and the standard deviation of joint angles was ± 2.65°. The developed microelectromechanical system–based tracking system provides an alternative low-cost solution to track joint movement. Moreover, it is able to operate on an Android platform and could potentially be used to assist outdoor or home-based rehabilitation.

The Correcting Approach of Gyroscope-Free Inertial Navigation Based on the Applicable Topological Map

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Mehdi Dehghani ◽  
Hamed Kharrati ◽  
Hadi Seyedarabi ◽  
Mahdi Baradarannia
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Topological Map ◽  
Stereo Cameras ◽  
Long Time ◽  
Navigation Errors

The accumulated error and noise sensitivity are the two common problems of ordinary inertial sensors. An accurate gyroscope is too expensive, which is not normally applicable in low-cost missions of mobile robots. Since the accelerometers are rather cheaper than similar types of gyroscopes, using redundant accelerometers could be considered as an alternative. This mechanism is called gyroscope-free navigation. The article deals with autonomous mobile robot (AMR) navigation based on gyroscope-free method. In this research, the navigation errors of the gyroscope-free method in long-time missions are demonstrated. To compensate the position error, the aid information of low-cost stereo cameras and a topological map of the workspace are employed in the navigation system. After precise sensor calibration, an amendment algorithm is presented to fuse the measurement of gyroscope-free inertial measurement unit (GFIMU) and stereo camera observations. The advantages and comparisons of vision aid navigation and gyroscope-free navigation of mobile robots will be also discussed. The experimental results show the increasing accuracy in vision-aid navigation of mobile robot.

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 The Design of a Low Cost Phasor Measurement Unit

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2648 ◽  
Author(s):  
Antonio Delle Femine ◽  
Daniele Gallo ◽  
Carmine Landi ◽  
Mario Luiso
Keyword(s):  
Power Systems ◽  
High Performance ◽  
Low Cost ◽  
Data Communication ◽  
Estimation Algorithm ◽  
Building Blocks ◽  
Processing Technique ◽  
Measurement Unit ◽  
Signal Processing Technique ◽  
Phasor Measurement

The widespread diffusion of Phasor Measurement Units (PMUs) is a becoming a need for the development of the “smartness” of power systems. However, PMU with accuracy compliant to the standard Institute of Electrical and Electronics Engineers (IEEE) C37.118.1-2011 and its amendment IEEE Std C37.118.1a-2014 have typically costs that constitute a brake for their diffusion. Therefore, in this paper, the design of a low-cost implementation of a PMU is presented. The low cost approach is followed in the design of all the building blocks of the PMU. A key feature of the presented approach is that the data acquisition, data processing and data communication are integrated in a single low cost microcontroller. The synchronization is obtained using a simple external Global Positioning System receiver, which does not provide a disciplined clock. The synchronization of sampling frequency, and thus of the measurement, to the Universal Time Coordinated, is obtained by means of a suitable signal processing technique. For this implementation, the Interpolated Discrete Fourier Transform has been used as the synchrophasor estimation algorithm. A thorough metrological characterization of the realized prototype in different test conditions proposed by the standards, using a high performance PMU calibrator, is also shown.

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