Indoor Mapping and Localization for a Smart Wheelchair Using Measurements of Ambient Magnetic Fields

2015 ◽  
Author(s):  
Anthony T. Trezza ◽  
Nurali N. Virani ◽  
Kelilah L. Wolkowicz ◽  
Jason Z. Moore ◽  
Sean N. Brennan
Keyword(s):  
Magnetic Fields ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Daily Lives ◽  
Motion Models ◽  
Working Environments ◽  
Kinematic Models ◽  
Mapping Techniques ◽  
Reliable Solution ◽  
Smart Wheelchair

Freedom of mobility is a crucial aspect of our daily lives. Consequently, engineering solutions for mobility, including smart wheelchairs, are becoming increasingly important for those with disabilities. However, the lack of a reliable solution for indoor localization has affected the pace of research in this direction. GPS signals cannot be measured indoors and environment modifications for wheelchair localization can be expensive and intrusive. This research explores the feasibility of using ambient magnetic fields for indoor localization by exploiting the spatial non-uniformity due to ferromagnetic objects in ordinary working environments. A non-parametric density estimation technique was developed to build magnetic field maps. This approach is compared to an existing regression technique. Two different approximate kinematic models for the wheelchair are presented and implemented in a particle-filtering framework. Finally, the efficacy of these mapping techniques and motion models, including and excluding odometry information, are compared via tracking experiments conducted with a smart wheelchair.

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

scholarly journals Can We Use Grip Strength to Predict Other Types of Hand Exertions? An Example of Manufacturing Industry Workers

2021 ◽  
Vol 18 (3) ◽  
pp. 856
Author(s):  
Victor Ei-Wen Lo ◽  
Yi-Chen Chiu ◽  
Hsin-Hung Tu
Keyword(s):  
Grip Strength ◽  
Manufacturing Industry ◽  
Prediction Models ◽  
Demographic Variable ◽  
Time Saving ◽  
Daily Lives ◽  
Working Environments ◽  
Central Taiwan ◽  
Study Participants ◽  
The Relationship

Background: There are different types of hand motions in people’s daily lives and working environments. However, testing duration increases as the types of hand motions increase to build a normative database. Long testing duration decreases the motivation of study participants. The purpose of this study is to propose models to predict pinch and press strength using grip strength. Methods: One hundred ninety-eight healthy volunteers were recruited from the manufacturing industries in Central Taiwan. The five types of hand motions were grip, lateral pinch, palmar pinch, thumb press, and ball of thumb press. Stepwise multiple linear regression was used to explore the relationship between force type, gender, height, weight, age, and muscle strength. Results: The prediction models developed according to the variable of the strength of the opposite hand are good for explaining variance (76.9–93.1%). Gender is the key demographic variable in the predicting models. Grip strength is not a good predictor of palmar pinch (adjusted-R2: 0.572–0.609), nor of thumb press and ball of thumb (adjusted-R2: 0.279–0.443). Conclusions: We recommend measuring the palmar pinch and ball of thumb strength and using them to predict the other two hand motions for convenience and time saving.

3D Indoor Localization using 5G-based Particle Filtering and CAD Plans

2021 ◽  
Author(s):  
Hossein Shoushtari ◽  
Cigdem Askar ◽  
Dorian Harder ◽  
Thomas Willemsen ◽  
Harald Sternberg
Keyword(s):  
Particle Filtering ◽  
Indoor Localization

STUPEFY: Set-Valued Box Particle Filtering for Bluetooth Low Energy-Based Indoor Localization

2019 ◽  
Vol 26 (12) ◽  
pp. 1773-1777 ◽  
Author(s):  
Parvin Malekzadeh ◽  
Arash Mohammadi ◽  
Mihai Barbulescu ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Particle Filtering ◽  
Indoor Localization ◽  
Low Energy ◽  
Bluetooth Low Energy

scholarly journals WI-FI RSS FINGERPRINTING FOR INDOOR LOCALIZATION USING AUGMENTED REALITY

2020 ◽  
Vol V-4-2020 ◽  
pp. 57-64
Author(s):  
A. Ahmad ◽  
P. Claudio ◽  
A. Alizadeh Naeini ◽  
G. Sohn
Keyword(s):  
Augmented Reality ◽  
Indoor Localization ◽  
Facility Management ◽  
Ease Of Use ◽  
Reference Points ◽  
Floor Plan ◽  
Indoor Space ◽  
Fingerprinting Method ◽  
Mapping Techniques ◽  
And Performance

Abstract. Indoor localization has attracted the attention of researchers for wide applications in areas like construction, facility management, industries, logistics, and health. The Received Signal Strength (RSS) based fingerprinting method is widely adopted because it has a lower cost over other methods. RSS is a measurement of the power present in the received radio signal. While this fingerprinting method is very popular, there is a significant amount of effort required for collecting fingerprints for indoor space. In this paper, we propose an RSS fingerprinting method using Augmented Reality (AR) that does not rely on an external sensor resulting in ease of use and maintenance. This method uses spatial mapping techniques to help align the floor plan of existing buildings; then, after the alignment, we map local device coordinates to global coordinates. After this process, we partition the space in equally distanced reference points for RSS fingerprint collection. We developed an application for Microsoft HoloLens to align the floor plan and collect fingerprints on reference points. Then we tested collected fingerprints with existing RSS based indoor localization methods for its accuracy and performance.

scholarly journals Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2818
Author(s):  
Ruolin Guo ◽  
Danyang Qin ◽  
Min Zhao ◽  
Xinxin Wang
Keyword(s):  
Particle Filtering ◽  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Dead Reckoning ◽  
Local Area ◽  
Area Network ◽  
Map Construction ◽  
Indoor Radio ◽  
Radio Map

The crowdsourcing-based wireless local area network (WLAN) indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps. Aiming at the problem of the diverse terminal devices and the inaccurate location annotation of the crowdsourced samples, which will result in the construction of the wrong radio map, an effective indoor radio map construction scheme (RMPAEC) is proposed based on position adjustment and equipment calibration. The RMPAEC consists of three main modules: terminal equipment calibration, pedestrian dead reckoning (PDR) estimated position adjustment, and fingerprint amendment. A position adjustment algorithm based on selective particle filtering is used by RMPAEC to reduce the cumulative error in PDR tracking. Moreover, an inter-device calibration algorithm is put forward based on receiver pattern analysis to obtain a device-independent grid fingerprint. The experimental results demonstrate that the proposed solution achieves higher localization accuracy than the peer schemes, and it possesses good effectiveness at the same time.

scholarly journals Indoor Localization by using Particle Filtering Approach with Wireless Sensor Nodes

2013 ◽  
Vol 9 (1) ◽  
pp. 74
Author(s):  
Hakan Koyuncu ◽  
Ahmet Çevik
Keyword(s):  
Particle Filtering ◽  
Indoor Localization ◽  
Propagation Model ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distance Measurements ◽  
Wireless Sensor Nodes ◽  
Object Distance ◽  
Object Locations ◽  
Current Estimation

Jennic type wireless sensor nodes are utilized together with a novel particle filtering technique for indoor localization. Target objects are localized with an accuracy of around 0.25 meters. The proposed technique introduces a new particle generation and distribution technique to improve current estimation of object positions. Particles are randomly distributed around the object in the sensing area within a circular strip of 2 STD of object distance measurements. Particle locations are related to object locations by using Gaussian weight distribution methods. Object distances from the transmitters are determined by using received RSSI values and ITU-R indoor propagation model. Measured object distances are used together with the particle distances from the transmitters to predict the object locations.

Vehicle Dynamics Models for Onboard Motion Planning

2014 ◽  
Author(s):  
Rudranarayan Mukherjee ◽  
Thomas Howard ◽  
Steven Myint ◽  
Johnny Chang ◽  
Jack Craft
Keyword(s):  
Motion Planning ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Adequate Model ◽  
Motion Models ◽  
Vehicle Data ◽  
Kinematic Models ◽  
Autonomous Mobility ◽  
Path Planner ◽  
Dynamics Models

Offline multibody dynamics based modeling and simulation of vehicle dynamics has been pursued with varying levels of success for more than two decades. This has been used in design, controls, training, and other technical and programmatic objectives. Over the last decade, autonomous vehicle dynamics has become an important area of research. This has resulted in a growing need for onboard vehicle model that works with the vehicle controller and path planner. Typically, kinematic models have largely been used for these objectives. Use of dynamics models for onboard motion planning is a relatively new topic of research with only a handful of prior work. In this paper we report our attempts at addressing the need for onboard vehicle dynamics models for motion planning in relatively fast autonomous mobility scenarios. We present the idea of using adaptive motion models that trade fidelity and cost of simulation to enable a motion planner to select an adequate model. Towards this, we present representative simulation results that demonstrate the need for adaptivity. We then present some technical challenges with onboard vehicle models and our attempts at addressing these challenges. Finally, we present some results that compare raw vehicle data with model predictive results.

scholarly journals A Review on Indoor Positioning Technologies and its Principles

2021 ◽  
Vol 9 (VII) ◽  
pp. 494-501
Author(s):  
Pradyumna C
Keyword(s):  
Indoor Localization ◽  
Indoor Positioning ◽  
Line Of Sight ◽  
Indoor Environments ◽  
Daily Lives ◽  
Positioning Systems ◽  
Wide Range ◽  
Outdoor Environments ◽  
Location Detection ◽  
Pass Through

This paper aims to provide the reader with a review of the main technologies present in the literature to solve the indoor localization problem that is indoor positioning without GPS. Location detection has been implemented very successfully in outdoor environments using GPS technology. GPS has had a great impact on our daily lives by supporting a large number of applications. However, in indoor environments, the availability of GPS or equivalent satellite-based positioning systems is limited due to the lack of line of sight and attenuation of the GPS signal when they pass through walls. The goal of this paper is to provide a technical perspective on indoor positioning systems, including a wide range of technologies and methods.

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