scholarly journals A Hybrid Framework for Mitigating Heading Drift for a Wearable Pedestrian Navigation System through Adaptive Fusion of Inertial and Magnetic Measurements

2021 ◽  
Vol 11 (4) ◽  
pp. 1902
Author(s):  
Liqiang Zhang ◽  
Yu Liu ◽  
Jinglin Sun
Keyword(s):  
Magnetic Measurements ◽  
Inertial Sensor ◽  
Angular Rate ◽  
Human Motion ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Hybrid Framework ◽  
Outdoor Environments ◽  
Adaptive Fusion ◽  
Indoor And Outdoor Environments

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.

scholarly journals Survey of Radio Navigation Systems

2015 ◽  
Vol 61 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Przemysław Gilski ◽  
Jacek Stefański
Keyword(s):  
Information Processing ◽  
Navigation Systems ◽  
Consumer Behavior Analysis ◽  
Radio Navigation ◽  
Pedestrian Navigation ◽  
Outdoor Environments ◽  
Wireless Positioning ◽  
Indoor And Outdoor Environments ◽  
Processing Mechanisms ◽  
Indoor And Outdoor

Abstract At present, there is a growing demand for radio navigation systems, ranging from pedestrian navigation to consumer behavior analysis. These systems have been successfully used in many applications and have become very popular in recent years. In this paper we present a review of selected wireless positioning solutions operating in both indoor and outdoor environments. We describe different positioning techniques, methods, systems, as well as information processing mechanisms

Inertial Sensing in Biomechanics

2011 ◽  
pp. 906-934
Author(s):  
Angelo M. Sabatini
Keyword(s):  
Independent Living ◽  
Inertial Sensor ◽  
Ambulatory Monitoring ◽  
Human Motion ◽  
Computational Techniques ◽  
Navigation Systems ◽  
Inertial Sensing ◽  
Current Location ◽  
Specialized Equipment ◽  
Physiological Sensors

Sensing approaches for ambulatory monitoring of human motion are necessary in order to objectively determine a person’s level of functional ability in independent living. Because this capability is beyond the grasp of the specialized equipment available in most motion analysis laboratories, body-mounted inertial sensing has been receiving increasing interest in the biomedical domain. Crucial to the success of this certainly not new sensing approach will be the capability of wearable inertial sensor networks to accurately recognize the type of activity performed (context awareness) and to determine the person’s current location (personal navigation), eventually in combination with other biomechanical or physiological sensors — key requirements in applications of wearable and mobile computing as well. This chapter reviews sensor configurations and computational techniques that have been implemented or are considered to meet the converging requirements of a wealth of application products, including ambulatory monitors for automatic recognition of activity, quantitative analysis of motor performance, and personal navigation systems.

The Standing Calibration Method of MEMS Gyro Bias for Autonomous Pedestrian Navigation System

2016 ◽  
Vol 70 (3) ◽  
pp. 607-617 ◽  
Author(s):  
Yanshun Zhang ◽  
Xu Yang ◽  
Xiangming Xing ◽  
Zhanqing Wang ◽  
Yunqiang Xiong
Keyword(s):  
Navigation System ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Calibration Method ◽  
Micro Electro Mechanical System ◽  
Human Motion ◽  
Pedestrian Navigation ◽  
Initial Stage ◽  
Angular Velocities ◽  
Gyro Bias

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.

scholarly journals Strapdown Sculling Velocity Algorithms Using Novel Input Combinations

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Huang ◽  
Zhaochun Li ◽  
Fei Xie ◽  
Kai Feng
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Angular Rate ◽  
Dynamic Environment ◽  
Navigation Systems ◽  
Specific Force ◽  
The Novel ◽  
Inertial Navigation Systems ◽  
Carrier Velocity ◽  
Digital Simulations

Sculling motion is a standard input to evaluate the performance of the velocity algorithm in a highly dynamic environment. Conventional sculling algorithms usually adopt incremental angle/specific force increments or angular rate/specific force as algorithm inputs. However modern inertial sensors have different output types now, which do not correspond to the inputs of those traditional algorithms. For example, some inertial sensors have the integrated angular rate (incremental angle)/specific force outputs or angular rate/specific force increments outputs. Hence the conventional sculling algorithms cannot be easily applied to these situations. A novel sculling algorithm using incremental angle/specific force inputs or angular rate/specific force increments inputs is developed in this paper. The advantage of the novel algorithm is that it can calculate the carrier velocity directly without converting the dimension of inertial sensor outputs values. Theoretical analysis, digital simulations, and a trial study are carried out to verify our algorithm. The results demonstrate that for corresponding types of strapdown inertial navigation systems (SINS) the novel sculling algorithm exhibits better performance than the conventional sculling velocity algorithms.

scholarly journals On the Benefits of Color Information for Feature Matching in Outdoor Environments

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 85
Author(s):  
Annika Hoffmann
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Robot Navigation ◽  
Navigation Systems ◽  
Color Information ◽  
Basic Technique ◽  
Outdoor Environments ◽  
Matching Performance ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

The detection and description of features is one basic technique for many visual robot navigation systems in both indoor and outdoor environments. Matched features from two or more images are used to solve navigation problems, e.g., by establishing spatial relationships between different poses in which the robot captured the images. Feature detection and description is particularly challenging in outdoor environments, and widely used grayscale methods lead to high numbers of outliers. In this paper, we analyze the use of color information for keypoint detection and description. We consider grayscale and color-based detectors and descriptors, as well as combinations of them, and evaluate their matching performance. We demonstrate that the use of color information for feature detection and description markedly increases the matching performance.

scholarly journals Depth-Camera-Aided Inertial Navigation Utilizing Directional Constraints

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5913
Author(s):  
Usman Qayyum ◽  
Jonghyuk Kim
Keyword(s):  
Degrees Of Freedom ◽  
Inertial Sensor ◽  
Visual Navigation ◽  
Depth Image ◽  
Epipolar Constraint ◽  
Detection Range ◽  
Outdoor Environments ◽  
Public Datasets ◽  
Indoor And Outdoor Environments ◽  
Directional Constraint

This paper presents a practical yet effective solution for integrating an RGB-D camera and an inertial sensor to handle the depth dropouts that frequently happen in outdoor environments, due to the short detection range and sunlight interference. In depth drop conditions, only the partial 5-degrees-of-freedom pose information (attitude and position with an unknown scale) is available from the RGB-D sensor. To enable continuous fusion with the inertial solutions, the scale ambiguous position is cast into a directional constraint of the vehicle motion, which is, in essence, an epipolar constraint in multi-view geometry. Unlike other visual navigation approaches, this can effectively reduce the drift in the inertial solutions without delay or under small parallax motion. If a depth image is available, a window-based feature map is maintained to compute the RGB-D odometry, which is then fused with inertial outputs in an extended Kalman filter framework. Flight results from the indoor and outdoor environments, as well as public datasets, demonstrate the improved navigation performance of the proposed approach.

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