IONet: Learning to Cure the Curse of Drift in Inertial Odometry

Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques.Comment: To appear in AAAI18 (Oral

Pedestrian localisation for indoor environments

Ubiquitous computing systems aim to assist us as we go about our daily lives, whilst at the same time fading into the background so that we do not notice their presence. To do this they need to be able to sense their surroundings and infer context about the state of the world. Location has proven to be an important source of contextual information for such systems. If a device can determine its own location then it can infer its surroundings and adapt accordingly. Of particular interest for many ubiquitous computing systems is the ability to track people in indoor environments. This interest has led to the development of many indoor location systems based on a range of technologies including infra-red light, ultrasound and radio. Unfortunately existing systems that achieve the kind of sub-metre accuracies desired by many location-aware applications require large amounts of infrastructure to be installed into the environment. This thesis investigates an alternative approach to indoor pedestrian tracking that uses on-body inertial sensors rather than relying on fixed infrastructure. It is demonstrated that general purpose inertial navigation algorithms are unsuitable for pedestrian tracking due to the rapid accumulation of errors in the tracked position. In practice it is necessary to frequently correct such algorithms using additional measurements or constraints. An extended Kalman filter is developed for this purpose and is applied to track pedestrians using foot-mounted inertial sensors. By detecting when the foot is stationary and applying zero velocity corrections a pedestrian’s relative movements can be tracked far more accurately than is possible using uncorrected inertial navigation. Having developed an effective means of calculating a pedestrian’s relative movements, a localisation filter is developed that combines relative movement measurements with environmental constraints derived from a map of the environment. By enforcing constraints such as impassable walls and floors the filter is able to narrow down the absolute position of a pedestrian as they move through an indoor environment. Once the user’s position has been uniquely determined the same filter is demonstrated to track the user’s absolute position to sub-metre accuracy. The localisation filter in its simplest form is computationally expensive. Furthermore symmetry exhibited by the environment may delay or prevent the filter from determining the user’s position. The final part of this thesis describes the concept of assisted localisation, in which additional measurements are used to solve both of these problems. The use of sparsely deployed WiFi access points is discussed in detail. The thesis concludes that inertial sensors can be used to track pedestrians in indoor environments. Such an approach is suited to cases in which it is impossible or impractical to install large amounts of fixed infrastructure into the environment in advance

Smartphone-Based Activity Recognition in a Pedestrian Navigation Context

In smartphone-based pedestrian navigation systems, detailed knowledge about user activity and device placement is a key information. Landmarks such as staircases or elevators can help the system in determining the user position when located inside buildings, and navigation instructions can be adapted to the current context in order to provide more meaningful assistance. Typically, most human activity recognition (HAR) approaches distinguish between general activities such as walking, standing or sitting. In this work, we investigate more specific activities that are tailored towards the use-case of pedestrian navigation, including different kinds of stationary and locomotion behavior. We first collect a dataset of 28 combinations of device placements and activities, in total consisting of over 6 h of data from three sensors. We then use LSTM-based machine learning (ML) methods to successfully train hierarchical classifiers that can distinguish between these placements and activities. Test results show that the accuracy of device placement classification (97.2%) is on par with a state-of-the-art benchmark in this dataset while being less resource-intensive on mobile devices. Activity recognition performance highly depends on the classification task and ranges from 62.6% to 98.7%, once again performing close to the benchmark. Finally, we demonstrate in a case study how to apply the hierarchical classifiers to experimental and naturalistic datasets in order to analyze activity patterns during the course of a typical navigation session and to investigate the correlation between user activity and device placement, thereby gaining insights into real-world navigation behavior

Indoor positioning of shoppers using a network of bluetooth low energy beacons

In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our node-graph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a “nearest-beacon” approach as a base-line; exponentially averaged weighted range estimates; and a particle-filter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilitie

Sensor Fused Indoor Positioning Using Dual Band WiFi Signal Measurements

A ubiquitous and accurate positioning system for mobile devices is of great importance both to business and research due to the large number of applications and services it enables. In most outdoor environments this problem was solved by the introduction of the Global Positioning System (GPS). In indoor or suburban areas however, the GPS signals are often too weak to enable a reliable position estimate. Instead, other techniques must be utilized to provide accurate positioning. One of these is trilateration based on WiFi signal strengths. This is an auspicious technology to use partly because of the large number of access points (APs) in our everyday environment, and partly due to the possibility of measuring signal strength with a normal smartphone. The technique is further enabled by the move to include transmitters at 2.4 as well as 5 GHz in modern APs, providing a better basis for accurate position estimations. Furthermore, the motion sensors present in today’s smartphones are accurate enough to provide a short-time estimate of the user’s movement with high accuracy. In this thesis, both of these technologies are used to develop an accurate method for indoor positioning, and the contributions can be summed up into two points. The first contribution is an investigation of the behavior of two WiFi frequencies, 2.4 and 5 GHz, where their time dependent noise is proven to be almost uncorrelated with each other. This is then exploited to develop aWiFi-only trilateration algorithm by the use of a particle filter (PF), where the only restriction is that the locations of the APs need to be known. The second contribution is adding an accelerometer and a gyroscope to the algorithm, to provide a more accurate estimation. A step counter is developed using the accelerometer, and the gyroscope detects changes in heading while the WiFi signal strengths give information about the position. This makes it possible to alongside the position also estimate both heading and step length, while still keeping the only restriction of knowing the AP locations. The resulting algorithm produces position estimates with a mean error less than two meters for a specific use case, and around three meters when a more lenient user behavior is allowed

사물인터넷을 위한 무선 실내 측위 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 김성철.실내 위치 기반 서비스는 스마트폰을 이용한 실내에서의 경로안내, 스마트 공장에서의 자원 관리, 실내 로봇의 자율주행 등 많은 분야에 접목될 수 있으며, 사물인터넷 응용에도 필수적인 기술이다. 다양한 위치 기반 서비스를 구현하기 위해서는 정확한 위치 정보가 필요하며, 적합한 거리 및 위치를 추정 기술이 핵심적이다. 야외에서는 위성항법시스템을 이용해서 위치 정보를 획득할 수 있다. 본 학위논문에서는 와이파이 기반 측위 기술에 대해 다룬다. 구체적으로, 전파의 신호 세기 및 도달 시간을 이용한 정밀한 실내 위치 추정을 위한 세 가지 기술에 대해 다룬다. 먼저, 비가시경로 환경에서의 거리 추정 정확도를 향상시켜 거리 기반 측위의 정확도를 향상시키는 하이브리드 알고리즘을 제안한다. 제안하 알고리즘은듀얼 밴드 대역의 신호세기를 감쇄량을 측정하여 거리 기반 측위 기법을 적용할 때, 거리 추정부 단계만을 데이터 기반 학습을 이용한 깊은 신경망 회귀 모델로 대체한 방안이다. 적절히 학습된 깊은 회귀 모델의 사용으로 비가시경로 환경에서 발생하는 거리 추정 오차를 효과적으로 감소시킬 수 있으며, 결과적으로 위치 추정 오차 또한 감소시켰다. 제안한 방법을 실내 광선추적 기반 모의실험으로 평가했을 때, 기존 기법들에 비해서 위치 추정 오차를 중간값을 기준으로 22.3% 이상 줄일 수 있음을 검증했다. 추가적으로, 제안한 방법은 실내에서의 AP 위치변화 등에 강인함을 확인했다. 다음으로, 본 논문에서는 비가시경로에서 단일 대역 수신신호세기를 측정했을 때 비가시경로가 많은 실내 환경에서 위치 추정 정확도를 높이기 위한 방안을 제안한다. 단일 대역 수신신호세기를 이용하는 방안은 기존에 이용되는 와이파이, 블루투스, 직비 등의 기반시설에 쉽게 적용될 수 있기 때문에 널리 이용된다. 하지만 신호 세기의 단일 경로손실 모델을 이용한 거리 추정은 상당한 오차를 지녀서 위치 추정 정확도를 감소시킨다. 이러한 문제의 원인은 단일 경로손실 모델로는 실내에서의 복잡한 전파 채널 특성을 반영하기 어렵기 때문이다. 본 연구에서는 실내 위치 추정을 위한 목적으로, 중첩된 다중 상태 경로 감쇄 모델을 새롭게 제시한다. 제안한 모델은 가시경로 및 비가시경로에서의 채널 특성을 고려하여 잠재적인 후보 상태들을 지닌다. 한 순간의 수신 신호 세기 측정치에 대해 각 기준 기지국별로 최적의 경로손실 모델 상태를 결정하는 효율적인 방안을 제시한다. 이를 위해 기지국별 경로손실모델 상태의 조합에 따른 측위 결과를 평가할 지표로서 비용함수를 정의하였다. 각 기지국별 최적의 채널 모델을 찾는데 필요한 계산 복잡도는 기지국 수의 증가에 따라 기하급수적으로 증가하는데, 유전 알고리즘을 이용한 탐색을 적용하여 계산량을 억제하였다. 실내 광선추적 모의실험을 통한 검증과 실측 결과를 이용한 검증을 진행하였으며, 제안한 방안은 실제 실내 환경에서 기존의 기법들에 비해 위치 추정 오차를 약 31% 감소시켰으며 평균적으로 1.92 m 수준의 정확도를 달성함을 확인했다. 마지막으로 FTM 프로토콜을 이용한 실내 위치 추적 알고리즘에 대해 연구하였다. 스마트폰의 내장 관성 센서와 와이파이 통신에서 제공하는 FTM 프로토콜을 통한 거리 추정을 이용하여 실내에서 사용자의 위치를 추적할 수 있다. 하지만 실내의 복잡한 다중경로 환경으로 인한 피크 검출 실패는 거리 측정치에 편향성을 유발한다. 또한 사용하는 디바이스의 종류에 따라 예상치 못한 거리 오차가 발생할 수있다. 본 논문에서는 실제 환경에서 FTM 거리 추정을 이용할 때 발생할 수 있는 오차들을 고려하고 이를 보상하는 방안에 대해 제시한다. 확장 칼만 필터와 결합하여 FTM 결과를 사전필터링 하여 이상값을 제거하고, 거리 측정치의 편향성을 제거하여 위치 추적 정확도를 향상시킨다. 실내에서의 실험 결과 제안한 알고리즘은 거치 측정치의 편향성을 약 44-65% 감소시켰으며 최종적으로 사용자의 위치를 서브미터급으로 추적할 수 있음을 검증했다.Indoor location-based services (LBS) can be combined with various applications such as indoor navigation for smartphone users, resource management in smart factories, and autonomous driving of robots. It is also indispensable for Internet of Things (IoT) applications. For various LBS, accurate location information is essential. Therefore, a proper ranging and positioning algorithm is important. For outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. It is necessary to develop a technology that can replace GNSS in GNSS-denied environments. Among the various alternative systems, the one of promising technology is to use a Wi-Fi system that has already been applied to many commercial devices, and the infrastructure is in place in many regions. In this dissertation, Wi-Fi based indoor localization methods are presented. In the specific, I propose the three major issues related to accurate indoor localization using received signal strength (RSS) and fine timing measurement (FTM) protocol in the 802.11 standard for my dissertation topics. First, I propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. I replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error. Next, I study on positioning algorithm that considering NLOS conditions for each APs, using single band RSS measurement. The single band RSS information is widely used for indoor localization because they can be easily implemented by using existing infrastructure like Wi-Fi, Blutooth, or Zigbee. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, I develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and NLOS states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, I formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, I apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92~m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09\% over the benchmark scenario. Finally, I investigate accurate indoor tracking algorithm using FTM protocol in this dissertation. By using the FTM ranging and the built-in sensors in a smartphone, it is possible to track the user's location in indoor. However, the failure of first peak detection due to the multipath effect causes a bias in the FTM ranging results in the practical indoor environment. Additionally, the unexpected ranging error dependent on device type also degrades the indoor positioning accuracy. In this study, I considered the factors of ranging error in the FTM protocol in practical indoor environment, and proposed a method to compensate ranging error. I designed an EKF-based tracking algorithm that adaptively removes outliers from the FTM result and corrects bias to increase positioning accuracy. The experimental results verified that the proposed algorithm reduces the average ofthe ranging bias by 43-65\% in an indoor scenarios, and can achieve the sub-meter accuracy in average route mean squared error of user's position in the experiment scenarios.Abstract i Contents iv List of Tables vi List of Figures vii 1 INTRODUCTION 1 2 Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-BandWi-Fi 6 2.1 Motivation 6 2.2 Preliminary 8 2.3 System model 11 2.4 Proposed Ranging Method 13 2.5 Performance Evaluation 16 2.5.1 Ray-Tracing-Based Simulation 16 2.5.2 Analysis of the Ranging Accuracy 21 2.5.3 Analysis of the Neural Network Structure 25 2.5.4 Analysis of Positioning Accuracy 26 2.6 Summary 29 3 Genetic Algorithm for Path Loss Model Selection in Signal Strength Based Indoor Localization 31 3.1 Motivation 31 3.2 Preliminary 34 3.2.1 RSS-based Ranging Techniques 35 3.2.2 Positioning Technique 37 3.3 Proposed localization method 38 3.3.1 Localization Algorithm with Overlapped Multi-State Path Loss Model 38 3.3.2 Localization with Genetic Algorithm-Based Search 41 3.4 Performance evaluation 46 3.4.1 Numerical simulation 50 3.4.2 Experimental results 56 3.5 Summary 60 4 Indoor User Tracking with Self-calibrating Range Bias Using FTM Protocol 62 4.1 Motivation 62 4.2 Preliminary 63 4.2.1 FTM ranging 63 4.2.2 PDR-based trajectory estimation 65 4.3 EKF design for adaptive compensation of ranging bias 66 4.4 Performance evaluation 69 4.4.1 Experimental scenario 69 4.4.2 Experimental results 70 4.5 Summary 75 5 Conclusion 76 Abstract (In Korean) 89박