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2018 | Book

Smartphone-Based Indoor Map Construction

Principles and Applications

Authors: Dr. Ruipeng Gao, Fan Ye, Guojie Luo, Jason Cong

Publisher: Springer Singapore

Book Series : SpringerBriefs in Computer Science

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About this book

This book focuses on ubiquitous indoor localization services, specifically addressing the issue of floor plans. It combines computer vision algorithms and mobile techniques to reconstruct complete and accurate floor plans to provide better location-based services for both humans and vehicles via commodity smartphones in indoor environments (e.g., a multi-layer shopping mall with underground parking structures). After a comprehensive review of scene reconstruction methods, it offers accurate geometric information for each landmark from images and acoustics, and derives the spatial relationships of the landmarks and rough sketches of accessible areas with inertial and WiFi data to reduce computing overheads. It then presents the authors’ recent findings in detail, including the optimization and probabilistic formulations for more solid foundations and better robustness to combat errors, several new approaches to promote the current sporadic availability of indoor location-based services, and a holistic solution for floor plan reconstruction, indoor localization, tracking, and navigation. The novel approaches presented are designed for different types of indoor environments (e.g., shopping malls, office buildings and labs) and different users. A valuable resource for researchers and those in start-ups working in the field, it also provides supplementary material for students with mobile computing and networking backgrounds.

Table of Contents

Frontmatter
Chapter 1. Introduction of Indoor Map Construction
Abstract
We describe the motivation and background of map construction for ubiquitous indoor location-based services, and then give an overview of this book and present how it is organized in the following chapters.
Ruipeng Gao, Fan Ye, Guojie Luo, Jason Cong
Chapter 2. Indoor Map Construction via Mobile Crowdsensing
Abstract
The lack of indoor maps is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this chapter, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, and then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators, stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1 \(\sim \) 2 m and 5 \(\sim \) 9\(^\circ \), while the hallway connectivity and connection areas between stories are 100% correct.
Ruipeng Gao, Fan Ye, Guojie Luo, Jason Cong
Chapter 3. Incremental Indoor Map Construction with a Single User
Abstract
Lacking of floor plans is a fundamental obstacle to ubiquitous indoor location-based services. Recent work have made significant progress to accuracy, but they largely rely on slow crowdsensing that may take weeks or even months to collect enough data. In this chapter, we propose Knitter that can generate accurate floor maps by a single random user’s one-hour data collection efforts, and demonstrate how such maps can be used for indoor navigation. Knitter extracts high-quality floor layout information from single images, calibrates user trajectories, and filters outliers. It uses a multi-hypothesis map fusion framework that updates landmark positions/orientations and accessible areas incrementally according to evidences from each measurement. Our experiments on three different large buildings and 30+ users show that Knitter produces correct map topology, and 90-percentile landmark location and orientation errors of \(3\sim 5\,\mathrm{m}\) and \(4\sim 6^\circ \), comparable to the state of the art at more than \(20\times \) speed up: data collection can finish in about one hour even by a novice user trained just a few minutes.
Ruipeng Gao, Fan Ye, Guojie Luo, Jason Cong
Chapter 4. Indoor Localization by Photo-Taking of the Environment
Abstract
Mainstream indoor localization technologies rely on RF signatures that require extensive human efforts to measure and periodically recalibrate signatures. The progress to ubiquitous localization remains slow. In this chapter, we explore Sextant, an alternative approach that leverages environmental reference objects such as store logos. A user uses a smartphone to obtain relative position measurements to such static reference objects for the system to triangulate the user location. Sextant leverages image matching algorithms to automatically identify the chosen reference objects by photo-taking, and we propose two methods to systematically address image matching mistakes that cause large localization errors. We formulate the benchmark image selection problem, prove its NP-completeness, and propose a heuristic algorithm to solve it. We also propose a couple of geographical constraints to further infer unknown reference objects. To enable fast deployment, we propose a lightweight site survey method for service providers to quickly estimate the coordinates of reference objects. Extensive experiments have shown that Sextant prototype achieves 2–5 m accuracy at 80-percentile, comparable to the industry state of the art, while covering a \(150\times 75\) m mall and \(300\times 200\) m train station requires a one-time investment of only 2–3 man-hours from service providers.
Ruipeng Gao, Fan Ye, Guojie Luo, Jason Cong
Chapter 5. Smartphone-Based Real-Time Vehicle Tracking in Indoor Parking Structures
Abstract
Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return. In this chapter, we propose VeTrack, a smartphone-only system that tracks the vehicle’s location in real time using the phone’s inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel “shadow” trajectory tracing method to accurately estimate phone’s and vehicle’s orientations despite their arbitrary poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps and turns) recognition methods reliable against noises, disturbances from bumpy rides, and even handheld movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, and collect data with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle’s real-time location with almost negligible latency, with error of \(2\sim 4\) parking spaces at the 80th percentile.
Ruipeng Gao, Fan Ye, Guojie Luo, Jason Cong
Metadata
Title
Smartphone-Based Indoor Map Construction
Authors
Dr. Ruipeng Gao
Fan Ye
Guojie Luo
Jason Cong
Copyright Year
2018
Publisher
Springer Singapore
Electronic ISBN
978-981-10-8378-5
Print ISBN
978-981-10-8377-8
DOI
https://doi.org/10.1007/978-981-10-8378-5

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