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.

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 Semi-Automating Low Back Compression Force Estimations with an Inertial Sensor

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 37
Author(s):  
Sam Gleadhill ◽  
Daniel James ◽  
Raymond Leadbetter ◽  
Tomohito Wada ◽  
Ryu Nagahara ◽  
...  
Keyword(s):  
Motion Capture ◽  
Injury Risk ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Three Dimensional ◽  
Compressive Force ◽  
Low Back ◽  
Compression Force ◽  
Injury Risk Factor ◽  
Capture Method

There are currently no evidence-based practical automated injury risk factor estimation tools to monitor low back compressive force in ambulatory or sporting environments. For this purpose, inertial sensors may potentially replace laboratory-based systems with comparable results. The objective was to investigate inertial sensor validity to monitor low back compression force. Thirty participants completed a series of lifting tasks from the floor. Back compression force was estimated using a hand calculated method, an inertial sensor method and a three-dimensional motion capture method. Results demonstrated that semi-automation with a sensor had a higher agreement with motion capture compared to the hand calculated method, with angle errors of less than six degrees and back compression force errors of less than 200 Newtons. It was concluded that inertial sensors are valid to implement for static low back compression force estimations.

scholarly journals The Factors Influencing the Accuracy of Head Position During Canalith Reposition Procedure Using 9 Axis Inertial Sensor

2020 ◽  
Vol 63 (4) ◽  
pp. 154-162
Author(s):  
Hyung Sun Hong ◽  
Ki Nam Kim ◽  
Chang Bin Yun ◽  
Jin Gu Kang ◽  
Hyun Ji Kim ◽  
...  
Keyword(s):  
Error Rate ◽  
Semicircular Canal ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Three Dimensional ◽  
Head Position ◽  
Average Error ◽  
Delayed Treatment ◽  
Epley Maneuver ◽  
Accurate Position

Background and Objectives The canalith reposition procedure (CRP) is used for the treatment of benign paroxysmal positional vertigo (BPPV) where the accuracy of position may affect the therapeutic efficacy. We investigate the accuracy of head position in CRP and its influencing factors during the procedure by measuring the position using inertial sensors and three dimensional remodeling.Subjects and Method We included 28 patients who were diagnosed as BPPV. To evaluate the accuracy of the CRP, we used the inertial sensor on the patient’s goggle used for videonystagmography. We evaluated the accuracy of the treatment compared to the textual treatment used during CRP. We also evaluated patient factors that affected the accuracy of head position as well as analyzing the correlation between the error rate and the successful treatment rate.Results While the average error rate was 12.6±5.8% for the PSCC group, it was 10.2±5.2% for the lateral semicircular canal (LSCC) group. For the posterior semicircular canal (PSCC) the group with body mass index (BMI), less than 25 patients had the lower error rate than the group with BMI greater than 25. There was no significant differences regarding the error rate according to BMI or age in the PSCC group. There is no significant differences regarding the error rate between those treated within 1 week and those over 1 week. For the LSCC delayed treatment group, there was no significant differences of error rate between the 1st and 2nd maneuver at each position.Conclusion For the Epley maneuver, the error rate of patients with high BMI is higher than those with low BMI. When the repeated barbeque maneuver was conducted, patients could have a more accurate position due to the learning effect. Care should be taken to ensure accurate CRP by considering various factors.

scholarly journals A Preliminary Test of Measurement of Joint Angles and Stride Length with Wireless Inertial Sensors for Wearable Gait Evaluation System

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Takashi Watanabe ◽  
Hiroki Saito ◽  
Eri Koike ◽  
Kazuki Nitta
Keyword(s):  
Evaluation System ◽  
Stride Length ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Preliminary Test ◽  
Lower Limbs ◽  
Sensor System ◽  
Wearable Sensor ◽  
Joint Angles ◽  
Optical Motion

The purpose of this study is to develop wearable sensor system for gait evaluation using gyroscopes and accelerometers for application to rehabilitation, healthcare and so on. In this paper, simultaneous measurement of joint angles of lower limbs and stride length was tested with a prototype of wearable sensor system. The system measured the joint angles using the Kalman filter. Signals from the sensor attached on the foot were used in the stride length estimation detecting foot movement automatically. Joint angles of the lower limbs were measured with stable and reasonable accuracy compared to those values measured with optical motion measurement system with healthy subjects. It was expected that the stride length measurement with the wearable sensor system would be practical by realizing more stable measurement accuracy. Sensor attachment position was suggested not to affect significantly measurement of slow and normal speed movements in a test with the rigid body model. Joint angle patterns measured in 10 m walking with a healthy subject were similar to common patterns. High correlation between joint angles at some characteristic points and stride velocity were also found adequately. These results suggested that the wireless wearable inertial sensor system could detect characteristics of gait.

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 Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach

2014 ◽  
Vol 68 (3) ◽  
pp. 434-452 ◽  
Author(s):  
Zhiwen Xian ◽  
Xiaoping Hu ◽  
Junxiang Lian
Keyword(s):  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Three Dimensional ◽  
Fast Response ◽  
Location Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Stereo Camera

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

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.

Validation of an Inertial Sensor System for Physical Therapists to Quantify Movement Coordination During Functional Tasks

2018 ◽  
Vol 34 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Lindsey Tulipani ◽  
Mark G. Boocock ◽  
Karen V. Lomond ◽  
Mahmoud El-Gohary ◽  
Duncan A. Reid ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Physical Therapists ◽  
Inertial Sensor ◽  
Angular Displacement ◽  
Movement Patterns ◽  
Sensor System ◽  
Inertial System ◽  
Camera System ◽  
Mean Differences ◽  
Angular Displacements

Physical therapists evaluate patients’ movement patterns during functional tasks; yet, their ability to interpret these observations consistently and accurately is unclear. Physical therapists would benefit from a clinic-friendly method for accurately quantifying movement patterns during functional tasks. Inertial sensors, which are inexpensive, portable sensors capable of monitoring multiple body segments simultaneously, are a relatively new rehabilitation technology. We sought to validate an inertial sensor system by comparing lower limb and lumbar spine kinematic data collected simultaneously with a commercial inertial sensor system and a motion camera system while 10 subjects performed functional tasks. Mean and peak segment angular displacement data were calculated and compared between systems. Mean angular displacement root mean square error between the systems across all tasks and segments was <5°. Mean differences in peak displacements were generally acceptable (<5°) for the femur, tibia, and pelvis segments for all tasks; however, the inertial system overestimated lumbar flexion compared to the motion camera system. These data suggest that the inertial system is capable of measuring angular displacements within 5° of a system widely accepted for its accuracy. Standardization of sensor placement, better attachment methods, and improvement of inertial sensor algorithms will further increase the accuracy of the system.

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