Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

Positioning Techniques with Smartphone Technology: Performances and Methodologies in Outdoor and Indoor Scenarios

Smartphone technology is widespread both in the academy and in the commercial world. Almost every people have today a smartphone in their pocket, that are not only used to call other people but also to share their location on social networks or to plan activities. Today with a smartphone we can compute our position using the sensors settled inside the device that may also include accelerometers, gyroscopes and magnetometers, teslameter, proximity sensors, barometer, and GPS/GNSS chipset. In this chapter we want to analyze the state-of-the-art of the positioning with smartphone technology, considering both outdoor and indoor scenarios. Particular attention will be paid to this last situation, where the accuracy can be improved fusing information coming from more than one sensor. In particular, we will investigate an innovative method of image recognition based (IRB) technology, particularly useful in GNSS denied environment, taking into account the two main problems that arise when the IRB positioning methods are considered: the first one is the optimization of the battery, that implies the minimization of the frame rate, and secondly the latencies due to image processing for visual search solutions, required by the size of the database with the 3D environment images

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75 th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Advanced Particle Filter Methods

This chapter presents a set of algorithmic methods based on particle filter heuristics. We start with an introduction to particle filters, which covers the main motivation and related works. Then, the generic framework for particle filter algorithm is presented, followed by two important use cases regarding indoor positioning and multitarget tracking; for both problems, modified particle filter algorithms are presented followed by experimental results, implementation remarks, and a discussion. Finally, a short list of conclusion and future work are presented

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

Multimodal Sensing for Robust and Energy-Efficient Context Detection with Smart Mobile Devices

Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly growing. There are already over 2 billion smartphone users worldwide [1] and the percentage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. This thesis presents how multimodal sensor data can be integrated to facilitate ro- bust and energy efficient mobile context detection, considering three important and challenging detection tasks: indoor localization, indoor-outdoor detection and human activity recognition. This thesis presents three methods for multimodal sensor inte- gration, each applied for a different type of context detection task considered in this thesis. These are gradually decreasing in design complexity, starting with a solution based on an engineering approach decomposing context detection to simpler tasks and integrating these with a particle filter for indoor localization. This is followed by man- ual extraction of features from different sensors and using an adaptive machine learn- ing technique called semi-supervised learning for indoor-outdoor detection. Finally, a method using deep neural networks capable of extracting non-intuitive features di- rectly from raw sensor data is used for human activity recognition; this method also provides higher degree of generalization to other context detection tasks. Energy efficiency is an important consideration in general for battery powered mo- bile devices and context detection is no exception. In the various context detection tasks and solutions presented in this thesis, particular attention is paid to this issue by relying largely on sensors that consume low energy and on lightweight computations. Overall, the solutions presented improve on the state of the art in terms of accuracy and robustness while keeping the energy consumption low, making them practical for use on mobile devices