Erratum to: Development of a low-cost wearable sensing glove with multiple inertial sensors and a light and fast orientation estimation algorithm

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.

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A Proposal of a Motion Measurement System to Support Visually Impaired People in Rehabilitation Using Low-Cost Inertial Sensors

Entropy ◽

10.3390/e23070848 ◽

2021 ◽

Vol 23 (7) ◽

pp. 848

Author(s):

Karla Miriam Reyes Leiva ◽

Milagros Jaén-Vargas ◽

Miguel Ángel Cuba ◽

Sergio Sánchez Lara ◽

José Javier Serrano Olmedo

Keyword(s):

Visually Impaired ◽

Inertial Sensors ◽

Personal Autonomy ◽

Low Cost ◽

Step Length ◽

Hand Position ◽

Visually Impaired People ◽

Gait Parameters ◽

Impaired Person ◽

Further Development

The rehabilitation of a visually impaired person (VIP) is a systematic process where the person is provided with tools that allow them to deal with the impairment to achieve personal autonomy and independence, such as training for the use of the long cane as a tool for orientation and mobility (O&M). This process must be trained personally by specialists, leading to a limitation of human, technological and structural resources in some regions, especially those with economical narrow circumstances. A system to obtain information about the motion of the long cane and the leg using low-cost inertial sensors was developed to provide an overview of quantitative parameters such as sweeping coverage and gait analysis, that are currently visually analyzed during rehabilitation. The system was tested with 10 blindfolded volunteers in laboratory conditions following constant contact, two points touch, and three points touch travel techniques. The results indicate that the quantification system is reliable for measuring grip rotation, safety zone, sweeping amplitude and hand position using orientation angles with an accuracy of around 97.62%. However, a new method or an improvement of hardware must be developed to improve gait parameters’ measurements, since the step length measurement presented a mean accuracy of 94.62%. The system requires further development to be used as an aid in the rehabilitation process of the VIP. Now, it is a simple and low-cost technological aid that has the potential to improve the current practice of O&M.

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Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype

Journal of Sensor and Actuator Networks ◽

10.3390/jsan7040042 ◽

2018 ◽

Vol 7 (4) ◽

pp. 42 ◽

Cited By ~ 4

Author(s):

Salil Goel ◽

Allison Kealy ◽

Bharat Lohani

Keyword(s):

Information Sharing ◽

Inertial Sensors ◽

Low Cost ◽

Wide Band ◽

Cooperative Networks ◽

Global Navigation Satellite Systems ◽

Cooperative Localization ◽

Cooperative Network ◽

Precise Positioning ◽

Assisted Navigation

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization.

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Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Journal of Sensors ◽

10.1155/2017/8542153 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Siwen Guo ◽

Jin Wu ◽

Zuocai Wang ◽

Jide Qian

Keyword(s):

Kalman Filter ◽

Process Model ◽

Attitude Determination ◽

Angular Rate ◽

Estimation Algorithm ◽

Measurement Model ◽

Estimation Accuracy ◽

Orientation Estimation ◽

Kinematic Equation ◽

Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

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Natural Locomotion Based on Foot-Mounted Inertial Sensors in a Wireless Virtual Reality System

Presence Teleoperators & Virtual Environments ◽

10.1162/pres_a_00236 ◽

2015 ◽

Vol 24 (4) ◽

pp. 298-321 ◽

Cited By ~ 4

Author(s):

Ernesto de la Rubia ◽

Antonio Diaz-Estrella

Keyword(s):

Virtual Reality ◽

Gait Cycle ◽

Inertial Sensors ◽

Low Cost ◽

Point Of View ◽

Handheld Devices ◽

Preliminary Evaluation ◽

Virtual Reality System ◽

Object Searching

Virtual reality has become a promising field in recent decades, and its potential now seems clearer than ever. With the development of handheld devices and wireless technologies, interest in virtual reality is also increasing. Therefore, there is an accompanying interest in inertial sensors, which can provide such advantages as small size and low cost. Such sensors can also operate wirelessly and be used in an increasing number of interactive applications. An example related to virtual reality is the ability to move naturally through virtual environments. This is the objective of the real-walking navigation technique, for which a number of advantages have previously been reported in terms of presence, object searching, and collision, among other concerns. In this article, we address the use of foot-mounted inertial sensors to achieve real-walking navigation in a wireless virtual reality system. First, an overall description of the problem is presented. Then, specific difficulties are identified, and a corresponding technique is proposed to overcome each: tracking of foot movements; determination of the user’s position; percentage estimation of the gait cycle, including oscillating movements of the head; stabilization of the velocity of the point of view; and synchronization of head and body yaw angles. Finally, a preliminary evaluation of the system is conducted in which data and comments from participants were collected.

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A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

International Journal of Navigation and Observation ◽

10.1155/2015/503814 ◽

2015 ◽

Vol 2015 ◽

pp. 1-18 ◽

Cited By ~ 11

Author(s):

Heikki Hyyti ◽

Arto Visala

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Low Cost ◽

Direction Cosine ◽

Estimation Algorithm ◽

Calibration Method ◽

Attitude Estimation ◽

Mems Sensors ◽

Acceleration Sensors ◽

Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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