Low-cost Inertial Measurement Unit Research Articles

Tracking 3-D Motion of Dynamic Objects Using Monocular Visual-Inertial Sensing

Six degree-of-freedom (6-DoF) visual tracking of dynamic objects is fundamental to a large variety of robotics and augmented reality (AR) applications. A key to this problem is accurate distance measurement of dynamic objects, which is usually obtained via stereo cameras, RGB-D sensors, or LiDARs. In this paper, however, we address the problem using only a monocular camera rigidly mounted with a low-cost inertial measurement unit. This is a light-weight, small-size, and low-cost solution, which is particularly suitable for tracking dynamic objects on drones or on mobile phones. Starting from a generic image-based two-dimensional tracker, we propose a novel method to resolve the object scale ambiguity in monocular vision in a geometric manner based on correlation analysis. This enables accurate metric three-dimensional tracking of arbitrary objects without requiring any prior knowledge about the object shape or size. We discuss the applicability by analyzing the observability condition and degenerated cases for object scale recovery. Simulation and real-world experimental results with ground truth comparison, along with AR application examples, demonstrate the feasibility of the proposed 6-DoF tracking method.

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

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.

3D Forest Mapping Using A Low-Cost UAV Laser Scanning System: Investigation and Comparison

Automatic 3D forest mapping and individual tree characteristics estimation are essential for forest management and ecosystem maintenance. The low-cost unmanned aerial vehicle (UAV) laser scanning (ULS) is a newly developed tool for cost-effectively collecting 3D information and attempts to use it for 3D forest mapping have been made, due to its capability to provide 3D information with a lower cost and higher flexibility than the standard ULS and airborne laser scanning (ALS). As the direct georeferenced point clouds may suffer from distortion caused by the poor performance of a low-cost inertial measurement unit (IMU), and 3D forest mapping using low-cost ULS poses a great challenge. Therefore, this paper utilized global navigation satellite system (GNSS) and IMU aided Structure-from-Motion (SfM) for trajectory estimation, and, hence, overcomes the poor performance of low-cost IMUs. The accuracy of the low-cost ULS point clouds was compared with the ground truth data collected by a commercial ULS system. Furthermore, the effectiveness of individual trees segmentation and tree characteristics estimation derived from the low-cost ULS point clouds were accessed. Experiments were undertaken in Dongtai forest farm, Yancheng City, Jiangsu Province, China. The results showed that the low-cost ULS achieved good point clouds quality from visual inspection and comparable individual tree segmentation results (P = 0.87, r = 0.84, F = 0.85) with the commercial system. Individual tree height estimation performed well (coefficient of determination (R2) = 0.998, root-mean-square error (RMSE) = 0.323 m) using the low-cost ULS. As for individual tree crown diameter estimation, low-cost ULS achieved good results (R2 = 0.806, RMSE = 0.195 m) after eliminating outliers. In general, such results illustrated the high potential of the low-cost ULS in 3D forest mapping, even though 3D forest mapping using the low-cost ULS requires further research.

A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation.

There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

Guaranteed Performance of Nonlinear Attitude Filters on the Special Orthogonal Group SO(3)

This paper proposes two novel nonlinear attitude filters evolved directly on the Special Orthogonal Group SO(3) able to ensure prescribed measures of transient and steady-state performance. The tracking performance of the normalized Euclidean distance of attitude error is trapped to initially start within a large set and converge systematically and asymptotically to the origin from almost any initial condition. The convergence rate is guaranteed to be less than the prescribed value and the steady-state error does not exceed a predefined small value. The first filter uses a set of vectorial measurements with the need for attitude reconstruction. The second filter instead uses only a rate gyroscope measurement and two or more vectorial measurements. These filters provide good attitude estimates with superior convergence properties and can be applied to measurements obtained from low cost inertial measurement units (IMUs). Simulation results illustrate the robustness and effectiveness of the proposed attitude filters with guaranteed performance considering high level of uncertainty in angular velocity along with body-frame vector measurements. Keywords: Attitude, estimate, estimator, observer, filter, nonlinear deterministic attitude filter, special orthogonal group, Euler angles, angle-axis, Rodrigues vector, mapping, parameterization, prescribed performance, representation, robust, Multiplicative Extended Kalman Filter, KF, EKF, MEKF, asymptotic stability, almost global asymptotic, noise, rotational matrix, identity, origin, orientation, body frame, inertial frame, rigid body, three dimensional, 3D, space, micro electromechanical systems, sensor, MEMS, roll, pitch, yaw, UAVs, QUAV, SVD, fixed, moving, vehicles, robot, robotic system, spacecraft, submarine, underwater vehicle, passive complementary filter, explicit complementary filter, autonomous, comparative study, SO(3).

Validation of K-Sense against indirect calorimetry and accelerometry

PurposeConsidering the difficulty of accurately estimating energy expenditure (EE) in habitual physical activity (PA), efforts to improve estimation accuracy are well warranted. The aim of the study was, first, to validate the K-Sense EE estimation system to improve EE estimation accuracy of human low-intensity activities, and, second, to compare K-Sense EE estimation values against ActiGraph (GT3X+) accelerometer-derived EE estimates.MethodsA comparative analysis investigated the K-Sense EE estimation values against indirect calorimetry and ActiGraph (GT3X+) EE estimates. A sample of 18 participants (age: 24.0 ± 5.2 years) performed eight sedentary/low intensity lifestyle activities, each wearing K-Sense with sensors attached to right wrist and ankle.ResultsThe K-Sense estimation accuracy ranged from 89.4% to 99.9%, outperforming ActiGraph equations, which were found to overestimate EE of these low-intensity activities, achieving 70.3% estimation accuracy at best. T-tests showed no statistically significant differences between K-Sense and indirect calorimetry values. Bland-Altman plots, however, illustrated an EE estimation error ranging from –9 to 7 kcal (with 95% confidence limits of agreement) among individuals.ConclusionsEE evaluation with low-cost inertial measurement units, such as those found in K-Sense, is a valid method in comparison with indirect calorimetry and ActiGraph accelerometry.

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

The stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20–30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil’s stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

A deep learning approach for final grasping state determination from motion trajectory of a prosthetic hand

Deep Learning has been gaining popularity due to its numerous implementations and continuous growing capabilities, including the prosthetics industry which has trend of evaluation towards the smart operational decision. The aim of this study is to develop a reliable decision-making system for prosthetic hands which is responsible to grasp or point an object located in the interaction area. In order to achieve this goal, we have exploited the measurements taken from a low-cost inertial measurement unit (IMU) and proposed a convolutional neural network-based decision-making system, which utilizes 9 distinct measurement variables as input, 3 axis accelerometer, 3 axis gyroscope and 3 axis magnetometer. The given experiments on this paper successfully identify that the deep learning approach produces reliably 99.2% accuracy on deciding the final prosthetic hand action by analyzing IMU sensor readings that represents motion trajectory of the hand.

Experimental Validation of Vehicle Velocity, Attitude and IMU Bias Estimation

Abstract This paper proposes an estimation method for the three dimensional velocity, and the roll and pitch angles of a land vehicle. The usage of low-cost sensors is required for applications in production vehicles. The proposed estimation method uses a low-cost inertial measurement unit (IMU) of which the biases are estimated. A nonlinear system is used to create an observer for simultaneous state and parameter estimation. Experimental results show the potential of this approach in a real world environment.

Positioning Accuracy of a Pipeline Surveying System Based on MEMS IMU and Odometer: Case Study

An inertial navigation system (INS) aided by odometers and a series of reference points has been extensively used to determine the location of pipeline inspection gauges (PIGs) in long-range gas and oil pipeline inspection. However, little attention has been paid to short-range urban underground pipeline surveying and the use of a low-cost inertial measurement unit (IMU) for location determination. The positioning performance of PIGs using low-cost microelectromechanical systems (MEMS) INS for short-range urban underground pipeline surveying applications has seldom been comprehensively evaluated. In this paper, we analyzed the positioning accuracy of a PIG based on a MEMS IMU and odometer through a real pipeline surveying case study. Numerous pipeline surveying cases, covering different pipeline lengths, pipe conditions, and surveying times, are included to achieve a practical conclusion with respect to the surveying accuracy. The results demonstrated that the maximum measurement repeatability errors of PIGs based on MEMS IMU and odometer do not exceed 0.25% and 0.1% of the pipeline length in the horizontal and vertical directions, respectively. These results not only provide powerful support for the PIG developers but also provide the expected PIG accuracy in urban underground pipeline surveying by using low-cost IMUs.

Benchmarking low-cost inertial measurement units for indoor localisation and navigation of AGVs

In the last decade, the landscape of distribution and warehousing has drastically changed and is increasingly dominated by automated guided vehicles (AGVs) transporting palletised goods. The benefits that AGVs brought to the factory floor among which the decreasing labour costs, increasing safety, accuracy and productivity were also noticed by other sectors such as manufacturing, retail, e-commerce and, even military and healthcare. Nevertheless, the high initial investment and maintenance costs of these systems still remain bottlenecks for small and medium-sized enterprises (SMEs), slowing down their transition towards Industry 4.0. This demands for cost-effective AGVs equipped with low-cost exteroceptive and proprioceptive sensors for indoor navigation and localisation in highly dynamic environments. Therefore, this work presents the benchmarking of three low-cost and one medium-cost inertial measurement units (IMUs) characterised by an oft-recurring static analysis extended with a dynamic analysis. The former is based on the Overlapping Allan Variance (OAVAR) method and compares various sensor metrics such as white noise, bias offset and bias instability. The latter compares step and frequency response parameters of the IMUs, while being subjected to frequency ranges similar to those observed during AGV operation. In addition, these low and medium-cost IMUs are mounted on a mobile platform and applied in an indoor use case with a view to implement simultaneous localisation and mapping (SLAM) for AGVs in production sites. The influence of these IMUs on SLAM is investigated based on absolute and relative map metrics. Finally, it can be stated that with regard to AGV operation, the low-cost IMUs perform equally well compared to the medium-cost IMU.

Performance evaluation on GNSS, wheel speed sensor, yaw rate sensor, and gravity sensor integrated positioning algorithm for automotive navigation system

The Global Navigation Satellite System (GNSS) positioning technique is widely used for the automotive navigation system since it can provide the stable and accurate position and velocity in the most road environments at an affordable price. However, the performance of GNSS positioning technique is degraded in certain areas, where GNSS signals are blocked by buildings and tunnel. To overcome this problem, the GNSS positioning technique should be integrated with dead reckoning (DR) sensors such as accelerometer, gyroscope, and odometer. Recently, the most passenger cars are equipped with the Advanced Driver Assistance System (ADAS) based on numerous sensors to improve safety and convenience in driving. Among sensors for the ADAS, vehicle dynamic sensors such as wheel speed sensor (WSS), yaw rate sensor (YRS), gravity sensor (GS) can be used for the DR algorithm since those sensors measure vehicle’s motions. Therefore, this paper evaluates the vehicle positioning algorithm that integrate the GNSS with a three-dimensional dead reckoning based on WSS, YRS, and GS. The vehicle positioning algorithm is implemented through the extended Kalman filter of a loosely-coupled mode. Performance was evaluated through tests carried out in real driving trajectory including various GNSS signals reception environments. It is found that the proposed algorithm can be an alternative solution to compensate the limitation of the GNSS positioning technique, without the use of a low-cost inertial measurement unit.

Predictive trajectory estimation during rehabilitative tasks in augmented reality using inertial sensors.

This paper presents a wireless kinematic tracking framework used for biomechanical analysis during rehabilitative tasks in augmented and virtual reality. The framework uses low-cost inertial measurement units and exploits the rigid connections of the human skeletal system to provide egocentric position estimates of joints to centimeter accuracy. On-board sensor fusion combines information from three-axis accelerometers, gyroscopes, and magnetometers to provide robust estimates in real-time. Sensor precision and accuracy were validated using the root mean square error of estimated joint angles against ground truth goniometer measurements. The sensor network produced a mean estimate accuracy of 2.81° with 1.06° precision, resulting in a maximum hand tracking error of 7.06 cm. As an application, the network is used to collect kinematic information from an unconstrained object manipulation task in augmented reality, from which dynamic movement primitives are extracted to characterize natural task completion in N = 3 able-bodied human subjects. These primitives are then leveraged for trajectory estimation in both a generalized and a subject-specific scheme resulting in 0.187 cm and 0.161 cm regression accuracy, respectively. Our proposed kinematic tracking network is wireless, accurate, and especially useful for predicting voluntary actuation in virtual and augmented reality applications.

A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors

With the development of micro-electromechanical systems (MEMS), miniaturized, low-power and low-cost inertial measurement units (IMUs) have been widely integrated into mobile terminals and smart wearable devices. This provides the prospect of a broad application for the inertial sensor-based pedestrian dead-reckoning (IPDR) systems. Especially for indoor navigation and indoor positioning, the IPDR systems have many unique advantages that other methods do not have. At present, a large number of technologies and methods for IPDR systems are proposed. In this paper, we have analyzed and outlined the IPDR systems based on about 80 documents in the field of IPDR in recent years. The article is structured in the form of an introduction-elucidation-conclusion framework. First, we proposed a general framework to explore the structure of an IPDR system. Then, according to this framework, the IPDR system was divided into six relatively independent subproblems, which were discussed and summarized separately. Finally, we proposed a graph structure of IPDR systems, and a sub-directed graph, formed by selecting a combined path from the start node to the end node, skillfully constitutes a technical route of one specific IPDR system. At the end of the article, we summarized some key issues that need to be resolved before the IPDR systems are widely used.

Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System.

Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).

Angular Position Estimation of an Inverted Pendulum Using Low-Cost IMUs

Seeking an affordable solution to measure a bicycle’s roll angle, we came across an Inertial Measurement Unit (IMU) BNO055 by Bosch, which contains 3-axis accelerometer, gyroscope, and magnetometer, and is advertised to produce “absolute orientation” by a built-in proprietary “Fusion” algorithm. We found another low-cost IMU, an MPU-9250 from InvenSense, which could also calculate absolute orientation via embedded Fusion software. Being unable to find information about dynamic characteristics of these IMUs in their datasheets, we sought to evaluate them under dynamic conditions, specifically in the estimation of roll angle. We constructed an inverted pendulum as a model of a bicycle, mounted both IMUs on it, and attached a potentiometer to measure actual angular position for reference. Additionally, as an alternative to the proprietary Fusion algorithms, we devised and implemented an Extended Kalman Filter, which, we hypothesized, would perform better than the proprietary Fusion algorithms, because our algorithm incorporated the kinematics of the inverted pendulum while the Fusion algorithm of the IMUs did not. In a series of experiments, we observed a significant time lag, about 0.05-0.1 second, in BNO055’s raw acceleration and gyro signals. The BNO055’s Fusion responded with similar lag and an offset of 0.5-3°; we also noticed rather unpredictable fluctuation in the output signals, possibly due to its “automatic calibration” feature, which cannot be disabled. The MPU-9250 exhibited better performance than the BNO055 in terms of raw acceleration signals and, particularly, gyro signals. The MPU-9250’s Fusion performed somewhat better than BNO055’s, typically showing lag of 0.03-0.06 sec and static offset of 0.5-1°. Our implementation of Kalman Filter based on MPU-9250 raw signal performed better than either Fusion algorithm, with about 0.02-0.03 second lag and 0.5-1° offset, supporting our hypothesis. Our next step is to experiment on an actual bicycle in motion.

Hybrid Machine Learning VDM for UAVs in GNSS-denied Environment

This paper presents a novel approach to enhance unmanned aerial vehicle (UAV) autonomous navigation, without adding extra load to the vehicle. The proposed approach employs the UAV vehicle dynamic model to aid the navigation estimation in a Global Navigation Satellite Systems (GNSS)-denied environment, without the need to model any part of the UAV, and avoids the requirement for special equipment during the modeling procedures typically required for vehicle dynamic model-aided navigation. Taking advantage of the available information from previous flights during availability of GNSS, and with the aid of a hybrid machine learning approach, the proposed technique is able to enhance the navigation accuracy during GNSS outage, despite the massive drift occurring from utilizing a low-cost inertial measurement unit (IMU) during the outage period. Different scenarios are investigated to prove the robustness of the proposed technique, and the results are compared to two types of IMUs with the aid of an inverse mechanization IMU simulator

Effect of Strapdown Integration Order and Sampling Rate on IMU-Based Attitude Estimation Accuracy.

This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).

A Fusion Method for Combining Low-Cost IMU/Magnetometer Outputs for Use in Applications on Mobile Devices.

This paper presents a fusion method for combining outputs acquired by low-cost inertial measurement units and electronic magnetic compasses. Specifically, measurements of inertial accelerometer and gyroscope sensors are combined with no-inertial magnetometer sensor measurements to provide the optimal three-dimensional (3D) orientation of the sensors’ axis systems in real time. The method combines Euler–Cardan angles and rotation matrix for attitude and heading representation estimation and deals with the “gimbal lock” problem. The mathematical formulation of the method is based on Kalman filter and takes into account the computational cost required for operation on mobile devices as well as the characteristics of the low-cost microelectromechanical sensors. The method was implemented, debugged, and evaluated in a desktop software utility by using a low-cost sensor system, and it was tested in an augmented reality application on an Android mobile device, while its efficiency was evaluated experimentally.

Calibration and Noise Identification of a Rolling Shutter Camera and a Low-Cost Inertial Measurement Unit.

A low-cost inertial measurement unit (IMU) and a rolling shutter camera form a conventional device configuration for localization of a mobile platform due to their complementary properties and low costs. This paper proposes a new calibration method that jointly estimates calibration and noise parameters of the low-cost IMU and the rolling shutter camera for effective sensor fusion in which accurate sensor calibration is very critical. Based on the graybox system identification, the proposed method estimates unknown noise density so that we can minimize calibration error and its covariance by using the unscented Kalman filter. Then, we refine the estimated calibration parameters with the estimated noise density in batch manner. Experimental results on synthetic and real data demonstrate the accuracy and stability of the proposed method and show that the proposed method provides consistent results even with unknown noise density of the IMU. Furthermore, a real experiment using a commercial smartphone validates the performance of the proposed calibration method in off-the-shelf devices.