In a smartphone, the global navigation satellite system (GNSS) receiver is dominant in providing position information for users. However, GNSS signals are vulnerable to the environment, i.e., city canyons and tunnels. Pedestrian dead reckoning (PDR) exploring human walking gaits is another effective method to determine pedestrian position. Accelerometers and gyroscope sensors are available in smartphones to detect pedestrians&#x2019; steps and carry out PDR. Aiming to improve smartphone position accuracy, we propose two different factor graph optimization (FGO)-PDR/GNSS integration models. First, the position from PDR and GNSS is integrated using FGO. Second, given the negative influences of the heading angle errors and the inconsistency between a smartphone and the pedestrian walking direction, PDR step length and GNSS are integrated using FGO. Android smartphones were utilized to collect different datasets and evaluate the proposed methods; experimental results indicated the superior performance of the proposed methods. 

Smartphone PDR/GNSS Integration via Factor Graph Optimization for Pedestrian Navigation

These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

\n Pedestrian dead reckoning (PDR) is widely used in handheld indoor positioning systems. However, low-cost inertial sensors built into smartphones provide poor-quality measurements, resulting in cumulative error which consists of heading estimation error caused by gyroscope and step length estimation error caused by an accelerometer. Learning more motion features through limited measurements is important to improve positioning accuracy. This paper proposes an improved PDR system using smartphone sensors. Using gyroscope, two motion patterns, walking straight or turning, can be recognised based on dynamic time warp (DTW) and thus improve heading estimation from an extended Kalman filter (EKF). Joint quasi-static field (JQSF) detection is used to avoid bad magnetic measurements due to magnetic disturbances in an indoor environment. In terms of periodicity of angular rate while walking, peak–valley angular velocity detection and zero-cross detection is combined to detect steps. A step-length estimation method based on deep belief network (DBN) is proposed. Experimental results demonstrate that the proposed PDR system can achieve more accurate indoor positioning.

An improved PDR system with accurate heading and step length estimation using handheld smartphone

A Systematic Mapping Framework for Backpack Mobile Mapping System in Common Monotonous Environments

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.

https://www.mdpi.com/1424-8220/21/12/4033/pdf

Smartphone-Based Inertial Odometry for Blind Walkers.

As an infrastructure-free positioning and navigation method, pedestrian dead reckoning (PDR) is still a research hotspot in the field of indoor localization. Step detection (SD) and stride length estimation (SLE) are two key components of PDR, and it is a challenging problem to apply SD and SLE to different walking patterns. Focusing on this problem, this paper proposes a robust SD and SLE method based on recognizing three walking patterns (i.e., Normal Walk, March in Place, and Quick Walk) using a smartphone. First, we propose a dynamic time warping–based peak prediction with zero-crossing detection to improve the SD accuracy. In particular, the proposed SD can accurately identify the starting and ending points of each step in the three walking patterns. Second, according to the extracted features of each step, a random forest algorithm with classification proofreading is used to recognize the three walking patterns. Finally, an improved SLE model is proposed for the different walking patterns to achieve a higher SLE accuracy. The experimental results show that, on average, the SD accuracy is about 97.9%, the recognition accuracy is about 98.4%, and the relative error of the estimated walking distance is about 3.0%, which outperforms those of the existing commonly used SD and SLE methods.

A Robust Step Detection and Stride Length Estimation for Pedestrian Dead Reckoning Using a Smartphone

The invention discloses an indoor positioning method for correcting PDR with a Bluetooth auxiliary technology and a method thereof. The system comprises ground nodes, an intelligent terminal, and a positioning server. The ground nodes can be used for detecting stampede and broadcasting an identification number of the Bluetooth nodes to the intelligent terminal; the intelligent terminal can be usedfor receiving the identification number of the Bluetooth nodes and sending the identification number to the positioning server; and the positioning server is characterized in that by responding an indoor positioning map request of the intelligent terminal through Internet, an indoor positioning map is sent to the intelligent terminal. The indoor positioning method can effectively improve the PDRaccumulative error, and can greatly increase the PDR positioning precision.

Indoor positioning method for correcting PDR with Bluetooth auxiliary technology and method thereof

The invention discloses a P2P indoor navigation system and a P2P indoor navigation method. The P2P indoor navigation system comprises a way leader reference path generation module, a path informationprocessing module, a follower navigation path generation module and a server; the path information processing module is used for detecting turning point movements, walking path lengths, course anglesand geomagnetism sequences of a way leader and a follower; the way leader reference path generation module is used for performing path centralization according to the turning point movements, the walking path length, the course angle and the geomagnetism sequence of the way leader to generate reference path information and uploading a centralized path and the reference path information to the server; and the follower navigation path generation module is used for matching the turning point movement, the walking path length, the course angle and the geomagnetism sequence of the follower with thecorresponding centralized path and reference path information in the server. The P2P indoor navigation system provided by the invention does not have a host or a client, can finish an indoor navigation function by utilizing an inertial sensor in a smart phone only, avoids deployment of a scene in advance and can provide navigation service in most scenes.

P2P indoor navigation system and P2P indoor navigation method

The invention discloses a walking mode recognition method based on machine learning. The walking mode recognition method comprises an offline training stage and an online recognition stage. In the offline training stage, accelerometer data and magnetometer data of pedestrians in the three walking modes of normal walking, in-situ stepping and jogging are collected respectively, and a classifier based on machine learning is trained; and in the online identification stage, the trained classifier is used for identifying the walking mode of the pedestrian in real time. According to the invention, the identification of the walking mode of in-situ stepping in the traditional mode identification field is made up. The three walking modes of normal walking, in-situ stepping and jogging can be accurately distinguished, and the classification precision is further improved through correction of the classifier. The method is combined with a PDR-based indoor positioning technology, so that the PDR technology can adapt to different walking modes of pedestrians, the positioning precision is improved, and the application range of the PDR technology is improved. In general, the technical principle ofthe invention is simple, and the method has good practicability and application prospect in reality.

Walking mode recognition method based on machine learning

The invention discloses a self-adaptive step length estimation method based on a pedestrian motion state. The method comprises the following steps: S1, an accelerometer arranged in the intelligent terminal collecting acceleration data and magnetometer data of pedestrians in the moving process; S2, preprocessing data; S3, dividing the period of data; S4, detecting the maximum value and the minimumvalue of the acceleration of each step of the pedestrian, and simultaneously calculating the walking frequency and the acceleration variance of each step of the pedestrian; S5, identifying the motionstate of each step of the pedestrian by the extracted features through a classifier; and S6, estimating the step length of the pedestrian by adopting a proper step length parameter according to the motion state identification result of the classifier. According to the method, a new nonlinear step length model is established to estimate the step length of the pedestrian, and the step length estimation precision is improved. Step length parameters of different motion states of the pedestrian are fitted through a least square method, and proper step length parameters are selected according to themotion state identification result of the classifier, so that the step length estimation model can adapt to different motion states of the pedestrian.

Self-adaptive step length estimation method based on pedestrian motion state

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Pan Lei

Author Tools

Chat about Author