scholarly journals Nonlinear Modeling of Azimuth Error for 2D Car Navigation Using Parallel Cascade Identification Augmented with Kalman Filtering

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Umar Iqbal ◽  
Jacques Georgy ◽  
Michael J. Korenberg ◽  
Aboelmagd Noureldin
Keyword(s):  
Kalman Filtering ◽  
Urban Areas ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Nonlinear Modeling ◽  
Land Vehicle ◽  
Bias Drift ◽  
Parallel Cascade Identification ◽  
Linear And Nonlinear ◽  
Car Navigation

Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in urban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and vehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial sensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed measurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates over time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors requires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade Identification (PCI) module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and residual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments in a land vehicle.

scholarly journals LiDAR/RISS/GNSS Dynamic Integration for Land Vehicle Robust Positioning in Challenging GNSS Environments

2020 ◽  
Vol 12 (14) ◽  
pp. 2323
Author(s):  
Ahmed Aboutaleb ◽  
Amr S. El-Wakeel ◽  
Haidy Elghamrawy ◽  
Aboelmagd Noureldin
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Three Dimensional ◽  
Sensor System ◽  
Extended Kalman Filtering ◽  
Land Vehicle ◽  
Position Errors ◽  
Gnss Receivers

The autonomous vehicles (AV) industry has a growing demand for reliable, continuous, and accurate positioning information to ensure safe traffic and for other various applications. Global navigation satellite system (GNSS) receivers have been widely used for this purpose. However, GNSS positioning accuracy deteriorates drastically in challenging environments such as urban environments and downtown cores. Therefore, inertial sensors are widely deployed inside the land vehicle for various purposes, including the integration with GNSS receivers to provide positioning information that can bridge potential GNSS failures. However, in dense urban areas and downtown cores where GNSS receivers may incur prolonged outages, the integrated positioning solution may become prone to severe drift resulting in substantial position errors. Therefore, it is becoming necessary to include other sensors and systems that can be available in future land vehicles to be integrated with both the GNSS receivers and inertial sensors to enhance the positioning performance in such challenging environments. This work aims to design and examine the performance of a multi-sensor system that fuses the GNSS receiver data with not only the three-dimensional reduced inertial sensor system (3D-RISS), but also with the three-dimensional point cloud of onboard light detection and ranging (LiDAR) system. In this paper, a comprehensive LiDAR processing and odometry method is developed to provide a continuous and reliable positioning solution. In addition, a multi-sensor Extended Kalman filtering (EKF)-based fusion is developed to integrate the LiDAR positioning information with both GNSS and 3D-RISS and utilize the LiDAR updates to limit the drift in the positioning solution, even in challenging or ultimately denied GNSS environment. The performance of the proposed positioning solution is examined using several road test trajectories in both Kingston and Toronto downtown areas involving different vehicle dynamics and driving scenarios. The proposed solution provided a performance improvement over the standalone inertial solution by 64%. Over a GNSS outage of 10 min and 2 km distance traveled, our solution achieved position errors less than 2% of the distance travelled.

A Comprehensive Overview of Inertial Sensor Calibration Techniques

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Shashi Poddar ◽  
Vipan Kumar ◽  
Amod Kumar
Keyword(s):  
High Precision ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Comprehensive Overview ◽  
Inertial Measurement ◽  
Calibration Techniques

Inertial measurement unit (IMU) comprising of the accelerometer and gyroscope is prone to various deterministic errors like bias, scale factor, and nonorthogonality, which need to be calibrated carefully. In this paper, a survey has been carried out over different calibration techniques that try to estimate these error parameters. These calibration schemes are discussed under two broad categories, that is, calibration with high-end equipment and without any equipment. Traditional calibration techniques use high-precision equipment to generate references for calibrating inertial sensors and are generally laboratory-based setup. Inertial sensor calibration without the use of any costly equipment is further studied under two subcategories: ones based on multiposition method and others with Kalman filtering framework. Later, a brief review of vision-based inertial sensor calibration schemes is also provided in this work followed by a discussion which indicates different shortcomings and future scopes in the area of inertial sensor calibration.

Implementation and Analysis of Tightly Integrated INS/Stereo VO for Land Vehicle Navigation

2017 ◽  
Vol 71 (1) ◽  
pp. 83-99 ◽  
Author(s):  
Fei Liu ◽  
Yashar Balazadegan Sarvrood ◽  
Yang Gao
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Local Level ◽  
Visual Odometry ◽  
Satellite System ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Land Vehicle ◽  
Tight Integration ◽  
Stereo Visual Odometry

Tight integration of inertial sensors and stereo visual odometry to bridge Global Navigation Satellite System (GNSS) signal outages in challenging environments has drawn increasing attention. However, the details of how feature pixel coordinates from visual odometry can be directly used to limit the quick drift of inertial sensors in a tight integration implementation have rarely been provided in previous works. For instance, a key challenge in tight integration of inertial and stereo visual datasets is how to correct inertial sensor errors using the pixel measurements from visual odometry, however this has not been clearly demonstrated in existing literature. As a result, this would also affect the proper implementation of the integration algorithms and their performance assessment. This work develops and implements the tight integration of an Inertial Measurement Unit (IMU) and stereo cameras in a local-level frame. The results of the integrated solutions are also provided and analysed. Land vehicle testing results show that not only the position accuracy is improved, but also better azimuth and velocity estimation can be achieved, when compared to stand-alone INS or stereo visual odometry solutions.

scholarly journals GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment

2016 ◽  
Vol 11 (1) ◽  
pp. 62 ◽  
Author(s):  
Mohammed Aftatah ◽  
Abdelkabir Lahrech ◽  
Abdelouahed Abounada ◽  
Aziz Soulhi
Keyword(s):  
Urban Areas ◽  
Inertial Sensor ◽  
Real Data ◽  
Vehicle Localization ◽  
Land Vehicle ◽  
Accurate Localization ◽  
Global Positioning ◽  
Proprioceptive Sensors ◽  
Sensor Errors ◽  
Fusion Approach

The main purpose of this paper is to present a fusion approach to bridge the period of Global Positioning System (GPS) outages using two proprioceptive sensors that are the Inertial Navigation System (INS) and the odometer in order to assure a continuous localization for land vehicle in urban areas where GPS signal blockage is very often. Odometer and GPS measures are exploited to correct inertial sensor errors. In fact, during GPS availability, INS is integrated with GPS to provide accurate localization solution; whereas during GPS outages, the odometer measurements are used to correct the INS error thereby improving the positioning accuracy and assuring the continuity of navigation solution. The problem of estimation of vehicle localization is realized by Kalman Filter (KF) that merges sensor measurements. The paper thus introduces results from simulation and real data.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

scholarly journals Smartphone-Based Inertial Odometry for Blind Walkers

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4033
Author(s):  
Peng Ren ◽  
Fatemeh Elyasi ◽  
Roberto Manduchi
Keyword(s):  
Particle Filtering ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Mean Shift ◽  
Gait Pattern ◽  
Comparative Assessment ◽  
Pedestrian Tracking ◽  
Step Counter ◽  
Assisted Navigation

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

scholarly journals Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5167
Author(s):  
Nicky Baker ◽  
Claire Gough ◽  
Susan J. Gordon
Keyword(s):  
Systematic Review ◽  
Discriminant Validity ◽  
Inertial Sensors ◽  
Postural Sway ◽  
Inertial Sensor ◽  
Dynamic Balance ◽  
Healthy Adults ◽  
Gait Velocity ◽  
Time Data ◽  
Targeted Interventions

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic balance in healthy adults. Medline, PubMed, Embase, Scopus, CINAHL, and Web of Science were searched to January 2021. Nineteen studies met the inclusion criteria. Meta-analysis was possible for reliability studies only and it was found that inertial sensors are reliable to measure static standing eyes open. A synthesis of the included studies shows moderate to good reliability for dynamic balance. Concurrent validity is moderate for both static and dynamic balance. Sensors discriminate old from young adults by amplitude of mediolateral sway, gait velocity, step length, and turn speed. Fallers are discriminated from non-fallers by sensor measures during walking, stepping, and sit to stand. The accuracy of discrimination is unable to be determined conclusively. Using inertial sensors to measure postural sway in healthy adults provides real-time data collected in the natural environment and enables discrimination between fallers and non-fallers. The ability of inertial sensors to identify differences in postural sway components related to altered performance in clinical tests can inform targeted interventions for the prevention of falls and near falls.

scholarly journals Finger and foot tapping sensor system for objective motor assessment

2018 ◽  
Vol 75 (1) ◽  
pp. 68-77 ◽  
Author(s):  
Milica Djuric-Jovicic ◽  
Nenad Jovicic ◽  
Sasa Radovanovic ◽  
Milica Jecmenica-Lukic ◽  
Minja Belic ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Motor Performance ◽  
Graphical Representation ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Quantitative Estimation ◽  
Objective Assessment ◽  
Force Sensor ◽  
Neurological Patient ◽  
Foot Tapping

Background/Aim. Finger tapping test is commonly used in neurological examinations as a test of motor performance. The new system comprising inertial and force sensors and custom proprietary software was developed for quantitative estimation and assessment of finger and foot tapping tests. The aim of this system was to provide diagnosis support and objective assessment of motor function. Methods. Miniature inertial sensors were placed on fingertips and used for measuring finger movements. A force sensor was placed on the fingertip of one finger, in order to measure the force during tapping. For foot tapping assessment, an inertial sensor was mounted on the subject?s foot, which was placed above a force platform. By using this system, various parameters such as a number of taps, tapping duration, rhythm, open and close speed, the applied force and tapping angle, can be extracted for detailed analysis of a patient?s motor performance. The system was tested on 13 patients with Parkinson?s disease and 14 healthy controls. Results. The system allowed easy measurement of listed parameters, and additional graphical representation showed quantitative differences in these parameters between neurological patient and healthy subjects. Conclusion. The novel system for finger and foot tapping test is compact, simple to use and efficiently collects patient data. Parameters measured in patients can be compared to those measured in healthy subjects, or among groups of patients, or used to monitor progress of the disease, or therapy effects. Created data and scores could be used together with the scores from clinical tests, providing the possibility for better insight into the diagnosis.

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