Vision-Aided Indoor Pedestrian Dead Reckoning

Vision-aided inertial navigation has become a more popular method for indoor positioning recently. This popularity is basically due to the development of light-weighted and low-cost Micro Electro-Mechanical Systems (MEMS) as well as advancement and availability of CCD cameras in public indoor area. While the use of inertial sensors and cameras are limited to the challenge of drift accumulation and object detection in line of sight, respectively, the integration of these two sensors can compensate their drawbacks and provide more accurate positioning solutions. This study builds up upon earlier research on “Vision-Aided Indoor Pedestrian Tracking System”, to address challenges of indoor positioning by providing more accurate and seamless solutions. The study improves the overall design and implementation of inertial sensor fusion for indoor applications. In this regard, genuine indoor maps and geographical information, i.e. digitized floor plans, are used for visual tracking application the pilot study. Both of inertial positioning and visual tracking components can work stand-alone with additional location information from the maps. In addition, while the visual tracking component can help to calibrate pedestrian dead reckoning and provides better accuracy, inertial sensing module can alternatively be used for positioning and tracking when the user cannot be detected by the camera until being detected in video again. The mean accuracy of this positioning system is 10.98% higher than uncalibrated inertial positioning during experiment

Wi-Fi Indoor Positioning System

Location tracking services are more attractive technologies in today’s world. These services make the use of wireless networks and broadband multimedia wireless networks to provide the location tracking services inside the buildings and campus areas. In this services determining the user’s current location or position accurately is the most important phenomena. Wi-Fi enabled indoor positioning technique is widely used in the outdoor environment to locate the persons moving inside the building and this technique is gaining popularity as all the android smart phones have this application. This technique is efficient in improving the positioning techniques. The aim of this project is to create an application to locate the position of the user inside thebuilding with more accuracy of the position of the user

2D-based indoor mobile laser scanning for construction digital mapping application

A common issue which occurs often in construction projects is how to determine the discrepancies between as-built or existing constructions and initial design. Physical manual measurement usually brings many of problems such as long measuring time, high labor consumption, and measurement error accumulation and in some cases lower accuracy. Therefore, more advanced technologies such as laser scanning and total station, which are used in geospatial mapping and surveying have been adopted in order to provide much more reliable and accurate measurements. However, technical and financial issues still constrain the widespread applications of well-known 3-dimensional (3D) terrestrial and aerial laser scanning, such as high equipment cost, complex pre-preparation, inconvenience of use and spatial limitation. This paper aims to introduce an innovative laser scanning method for indoor construction mapping. This method integrates an IMU-GPS positioning approach with a more convenient, more time saving and lower costed 2-dimensional (2D) laser scanner to realize indoor mobile 3D mapping for construction model creation, which can be integrated with Building Information Modelling (BIM) design in order to realize the applications, such as quality control of as-built construction or indoor mapping of existing building. Although compared with traditional 3D laser scanning, its accuracy and reliability cannot reach such a high level currently, experimental results still indicate feasibility, reliability and potential capability of this indoor mobile laser scanning method. It is hoped that this method will be further improved to substitute the stationary 3D laser scanning for narrow and limited construction spatial mapping in the near future

Indoor pedestrian dead reckoning calibration by visual tracking and map information

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data

Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points

SENSOR FUSION AND TEMPORAL INTEGRATION FOR TOUCH INTERFACE INDOOR POSITIONING

Dalam kunjungan wisata atau budaya, panduan terhadap objek menarik sangat berguna untuk menambah pengetahuan dan pengalaman pengunjung di lokasi tersebut. Dewasa ini, dengan bantuan teknologi modern, aplikasi bergerak mampu menjadi pemandu wisata mandiri otomatis dengan sistem sadar konteks. Kebanyakan, unsur konteks yang digunakan dalam aplikasi-aplikasi ini adalah posisi dua dimensi (2D). Meskipun begitu, ada beberapa kemungkinan lain agar tiap unsur konteks dari perangkat pintar ini dapat diteliti lebih lanjut. Berkat sensor dari ponsel pintar, konteks-konteks tersebut, yang terdiri dari konteks dalam 3 dimensi (3D) dari posisi dan orientasi (dalam sumbu X, Y, dan Z), dapat ditangkap oleh ponsel pintar. Dimensi-dimensi ini akan diteliti untuk mendapatkan kemungkinan keberhasilan digunakannya ponsel pintar yang digenggam sebagai pointer terhadap objek menarik. Hal ini dilakukan karena posisi 2D tidak bisa menangani konteks ketinggian. Sehingga, pengalaman pengguna dapat ditingkatkan karena mereka tidak terhalang secara visual dan audio. Tetapi, sensor-sensor ini memiliki galat pengukuran yang tinggi. Sehingga, suatu penggabungan sensor diterapkan untuk menangani galat tersebut. Penelitian ini menerapkan metode untuk memperkirakan orientasi sudut dan posisi dengan berbagai filter, yakni Complementary Filter dan Kalman Filter. Complementary Filter melibatkan gyroscope, magnetometer, dan accelerometer dari sensor inersial ponsel pintar. Sedangkan, Kalman Filter melibatkan accelerometer dan hasil Wi-Fi fingerprinting yang didapatkan dari pengamatan lingkungan. Evaluasi perkiraan-perkiraan hasil penggabungan observasi sensor oleh filter-filter tersebut menggunakan ilustrasi grafis dan evaluasi statistika untuk mengukur kualitas reduksi galat dari tiap filter. Hasil dari performa filter menunjukkan bahwa kualitas perkiraan orientasi oleh Complementary Filter cukup baik untuk menghasilkan sudut yang sesuai. Namun, perkiraan posisi oleh Kalman Filter menunjukkan hasil yang kurang baik akibat integrasi ganda terhadap derau dan pengaruh besar Wi-Fi fingerprinting. Hasil Wi-Fi fingerprinting menunjukkan perkiraan posisi yang tidak akurat. Hal ini menunjukkan bahwa perkiraan posisi tidak dapat digunakan dalam penelitian ini. Sedangkan, dalam percobaan menunjuk objek di laboratorium, perkiraan orientasi sudut memberikan hasl yang cukup baik dengan ponsel pintar. Secara ringkas, perkiraan posisi dan orientasi 3D dengan Complementary Filter dan Kalman Filter dalam ponsel untuk pointer tidak dapat digunakan menurut penelitian ini. Meskipun begitu, masih perlu diteliti mengenai penerapan filter lainnya untuk perkiraan posisi dan observasi lain untuk membantu perkiraan yang baik. Walaupun penggunaan filter dan observasi lain dapat mengorbankan sumber daya dari ponsel pintar. ======================================================================================================== During cultural or tourism visits, a guide of the interesting objects is useful to enhance the knowledge and the experience of the visitors. Nowadays, because of the modern technologies, mobile applications are capable to be a personal autonomous guide in the case of context-aware system. Mostly, the context element used in these applications is the position in two dimension (2D). However, there are more possibilities using the context elements from smartphone that can be explored. Thanks to smartphone sensors, the contexts which can be captured by smartphone are composed in 3 dimensions (3D) of both position and orientation (in X, Y, and Z axes). Those dimensions are used to explore the feasibility of smartphone which can held by hand as pointer to interesting objects, which can’t be handled by 2D position only. Thus, the user experience can be enhanced, as they don’t get vision-blocked or audio-blocked. However, those sensors have erroneous measurements. Hence, a sensor fusion is applied to overcome this drawback. The sensor fusion can be implemented not only using the internal smartphone sensors, but also the external environment. In this case of indoor environment, the Wi-Fi fingerprinting approach, which widely used as indoor positioning algorithm, can be considered as external observation. Even though so, the quality of the fusion should be studied to assure that it is feasible to use smartphone a pointing device in indoor environment. This study proposed a method to estimate orientation and position using different filters, namely Complementary Filter and Kalman Filter respectively. The complementary filter involves the gyroscope, magnetometer, and accelerometer from the smartphone inertial navigation sensors, while the Kalman Filter involves accelerometer and the Wi-Fi fingerprinting result which come from environmental measurement. To evaluate these estimations, the graphical representation and statistical evaluation are used to measure the filters’ quality in reducing the errors. The results of the filters’ performance showed that orientation estimation was adequate to give acceptable angle. But, unfortunately, position estimation had resulted in poor performance because of the double integration toward noise and the heavy influence from Wi-Fi fingerprinting. The Wi-Fi fingerprinting resulted inaccurate positioning. This concluded that the position estimation cannot be used at all in this study. In laboratory object pointing field experiment, the orientation estimation gave passable estimation to locate an object by a fixed smartphone position. To sum up, the 3D position and orientation estimation using Complementary Filter and Kalman Filter might not be feasible according to this study. However, regarding to 3D position estimation, possibly there are other methods than Kalman Filter which might be used as state estimator. And also, there are various external measurements which might help to achieve better estimation. Although, the drawbacks between the more sophisticated methods and the computation power and capability of smartphone should be considered for a good user experience

Pedometri per smartphone: analisi, implementazione e confronto dei modelli proposti in letteratura

Il presente elaborato è stato realizzato per fornire un'analisi dettagliata degli studi relativi all'implementazione di pedometri realizzati tramite l'utilizzo dei sensori presenti all'interno di smartphone Android. La ricerca ha avuto lo scopo di fornire un riassunto di tutte le possibili scelte implementative proposte all'interno di questi studi, confrontandole tra loro ed evidenziandone le ridondanze, e fornire un'analisi sull'effettiva efficacia di ognuna di esse. Per ottenere questo tipo di informazioni è stato eseguito un lavoro diviso in quattro differenti fasi. Durante la prima fase è stata realizzata un'approfondita ricerca dei principali studi relativi all'argomento appena descritto, ottenendo un importante quantitativo di informazioni relative all'attuale stato dell'arte. Durante la seconda fase è stata invece realizzata una dettagliata Tassonomia, ovvero uno schema contenente tutti i principali step proposti dalle diverse implementazioni, fornendo in questo modo una chiara visualizzazione delle diverse opzioni di utilizzo degli strumenti proposti. Durante la terza fase è stata quindi sviluppata una specifica applicazione Android attraverso la quale è possibile replicare tutti i diversi strumenti proposti, con la possibilità di combinarli tra loro in ogni possibile combinazione. Durante l'ultima fase è stato infine possibile testare, attraverso l'applicazione appena descritta, tutte le diverse implementazioni proposte all'interno dei differenti studi. Tramite l'utilizzo di specifici test è stato infatti possibile raccogliere un quantitativo di dati sufficiente a trarre conclusioni sulla reale efficacia delle varie implementazioni. Questi dati hanno quindi permesso di identificare i punti di forza ed i limiti di ciascuna implementazione, e di determinare quale di esse fornisca effettivamente i risultati migliori

A review of smartphones based indoor positioning: challenges and applications

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients