S3E: A Large-scale Multimodal Dataset for Collaborative SLAM

With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E

OCR-RTPS: An OCR-based real-time positioning system for the valet parking

Obtaining the position of ego-vehicle is a crucial prerequisite for automatic control and path planning in the field of autonomous driving. Most existing positioning systems rely on GPS, RTK, or wireless signals, which are arduous to provide effective localization under weak signal conditions. This paper proposes a real-time positioning system based on the detection of the parking numbers as they are unique positioning marks in the parking lot scene. It does not only can help with the positioning with open area, but also run independently under isolation environment. The result tested on both public datasets and self-collected dataset show that the system outperforms others in both performances and applies in practice. In addition, the code and dataset will release later.Comment: 25 pages, 9 figure

HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR

We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments. Using only body-mounted IMUs and LiDAR, HSC4D is space-free without any external devices' constraints and map-free without pre-built maps. Considering that IMUs can capture human poses but always drift for long-period use, while LiDAR is stable for global localization but rough for local positions and orientations, HSC4D makes both sensors complement each other by a joint optimization and achieves promising results for long-term capture. Relationships between humans and environments are also explored to make their interaction more realistic. To facilitate many down-stream tasks, like AR, VR, robots, autonomous driving, etc., we propose a dataset containing three large scenes (1k-5k $m^2$) with accurate dynamic human motions and locations. Diverse scenarios (climbing gym, multi-story building, slope, etc.) and challenging human activities (exercising, walking up/down stairs, climbing, etc.) demonstrate the effectiveness and the generalization ability of HSC4D. The dataset and code are available at http://www.lidarhumanmotion.net/hsc4d/.Comment: 10 pages, 8 figures, CVPR202

Enabling Multi-LiDAR Sensing in GNSS-Denied Environments: SLAM Dataset, Benchmark, and UAV Tracking with LiDAR-as-a-camera

The rise of Light Detection and Ranging (LiDAR) sensors has profoundly impacted industries ranging from automotive to urban planning. As these sensors become increasingly affordable and compact, their applications are diversifying, driving precision, and innovation. This thesis delves into LiDAR's advancements in autonomous robotic systems, with a focus on its role in simultaneous localization and mapping (SLAM) methodologies and LiDAR as a camera-based tracking for Unmanned Aerial Vehicles (UAV). Our contributions span two primary domains: the Multi-Modal LiDAR SLAM Benchmark, and the LiDAR-as-a-camera UAV Tracking. In the former, we have expanded our previous multi-modal LiDAR dataset by adding more data sequences from various scenarios. In contrast to the previous dataset, we employ different ground truth-generating approaches. We propose a new multi-modal multi-lidar SLAM-assisted and ICP-based sensor fusion method for generating ground truth maps. Additionally, we also supplement our data with new open road sequences with GNSS-RTK. This enriched dataset, supported by high-resolution LiDAR, provides detailed insights through an evaluation of ten configurations, pairing diverse LiDAR sensors with state-of-the-art SLAM algorithms. In the latter contribution, we leverage a custom YOLOv5 model trained on panoramic low-resolution images from LiDAR reflectivity (LiDAR-as-a-camera) to detect UAVs, demonstrating the superiority of this approach over point cloud or image-only methods. Additionally, we evaluated the real-time performance of our approach on the Nvidia Jetson Nano, a popular mobile computing platform. Overall, our research underscores the transformative potential of integrating advanced LiDAR sensors with autonomous robotics. By bridging the gaps between different technological approaches, we pave the way for more versatile and efficient applications in the future

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

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

Metoda za določanje položaja v prostoru na osnovi signalov WiFi in modela zgradbe

WiFi indoor localization is a difficult task due to the variability of the WiFi signal. Consequently, there have been many attempts to develop WiFi-based methods which were aided by some other means to provide accurate indoor localization. Technologies like dead reckoning and IMU sensors, crowd utilization and pattern matching, specialized Li-Fi hardware and directional antennas, etc. were used to aid the WiFi in order to develop more accurate and stable methods. The main disadvantage of such methods lies in difficult deployments due to technologies and requirements: Dead-reckoning-aided methods are not suitable for stationary objects, methods leveraging groups of people and many individuals are not best suited for home environment, Li-Fi assisted methods require mobile terminals to provide Li-Fi connectivity and therefore rule out mobile phones as the most common terminal. In the past, many fingerprinting methods were proposedthese require a survey in the area of localization during the setup phase. Unfortunately, the majority of fingerprinting-based methods do not address issues of long-term stability of the WiFi signals. Thus, they face accuracy issues a few days after the calibrationfrequent, costly and time-consuming recalibration procedures are used to address these issues. Model-based methods try to eliminate calibration procedures by simulating signal propagation. Many of the methods assume at least some parameters of propagation as fixed and therefore poorly address the issues of WiFi’s variability and long-term stability. A pure WiFi model-based method that successfully addresses these issues and requires a mobile terminal only for emitting or receiving the WiFi signals is the ultimate goal of the WiFi indoor localization. This thesis presents a novel indoor localization method, with the main intent of addressing the issues of real-world applicability. Therefore, we focused on developing a method with accuracy comparable to the state-of-the-art methods, while reducing the complexity of deployment and minimizing the required maintenance for long-term deployments. The presented method is a model-based method, implementing self-adaptive operability, i.e. it does not require any human intervention. The thesis discusses in detail the topics of the long-term stability of the WiFi signal, receiving vs. transmitting methods, the future WiFi standards, comparability of the methods and architectural aspects with respect to real-world applicability of the localization methods. Our presented method estimates the parameters of signal propagation, by knowing the positions of the access points, the architectural floor plan with the dividing walls and by monitoring power of the packets travelling between the access points. From this data propagation parameters defined in propagation model are inferred in an online manner. A device trying to define its position captures power information of the packets sent by the access points. Devices’ information on the observed power is used to determine its position by an algorithm run on the localization server. The presented WiFi method is primarily developed and evaluated in single- and multi-room office environments. The method’s ability to be easily applicable in any environment is emphasized by its evaluation in two different environments – office and residential. Between the two, no parameters were modified, thus evaluations indicate universality of the method. Furthermore, we provide evaluation also in narrow hallway because in the field of indoor localization such evaluation environments are common practice. During the evaluation of our proposed method in the office environment, we obtained an average error of 2.63 m and 3.22 m for the single- and multi-room environments respectively. Second evaluation was performed in the residential environment, for which the method or any of the parameters were not modified. Our method achieved an average evaluation error of 2.65 m with standard deviation of 1.51 m, during the four independent evaluations, each consisting of 17 localization points. High accuracy of localization, with acknowledgement to the intricate and realistic multi-room floor plan with different types of walls, realistic furniture and real-world signal interference from the neighboring apartments, proves the method’s applicability to the real-world environment. Evaluation accuracy can be compared to the state-of-the-art methods, while our easily-applicable method requires far less complicated setup procedures and/or hardware requirements. In the second part of the thesis, we generalize the WiFi method to be applicable to the frequencies other than 2.4 GHz WiFi. By defining a fusion algorithm which considers accuracy of the individual frequencies, we have defined the MFAM method: Multiple Frequency Adaptive Model-Based Indoor Localization Method. The MFAM is one of the first purely model-based approaches capable of utilizing multiple frequencies simultaneously. The MFAM method was evaluated in residential environment on two frequency bands: 868 MHz and 2.4 GHz. The method retained positive properties of our WiFi approach (e.g. pure model-based, self-adaptive operability, wide applicability on affordable hardware), while improving the accuracy due to multi-frequency fusion. The usage of multiple frequencies improved the average error of localization from 2.65 m, while using only the WiFi, down to 2.16 m, in the case of multi-frequency fusion, thus improving localization accuracy for 18%. Similar improvements were observed also for the standard deviation. Although the accuracy of the presented WiFi and MFAM methods is comparable if not better than the state-of-the-art methods, one of the most important achievements of our work is the applicability of the method to the real-world situations and its long-term stability. The definition of our method ensures that the accuracy of the method will be the same at the time it is initialized, as well as days later, without any human interaction.Določanje lokacije znotraj prostorov na podlagi WiFi signalov je zaradi variabilnosti signala WiFi težka naloga. Posledično je bilo v preteklosti veliko poizkusov razvoja WiFi metod, ki uporabljajo dodatne informacije za natančno lokalizacijo. Ocena prehojene poti in inercijski senzorji, uporaba množice ljudi in ujemanje vzorcev, tehnologija Li-Fi in usmerjene antene itd. je le nekaj v preteklosti uporabljenih načinov za dopolnitev WiFi signalov pri razvoju natančnih in stabilnih metod. Glavna slabost takih metod se kaže v zahtevnem uvajanju zaradi uporabljenih tehnologij in zahtev: metode ocene prehojene poti niso primerne za stacionarne predmete, metode, ki uporabljajo množice ljudi, niso primerne za domače okolje, Li-Fi metode zahtevajo, da so mobilni terminali opremljeni z ustreznimi sprejemniki in tako izključijo mobilne telefone kot terminale. V preteklosti so bile predlagane številne metode, ki bazirajo na prstnih odtisih signalov. Te metode zahtevajo kalibracijske meritve v prostoru v fazi implementacije metode. Večina teh metod ne naslovi vprašanj dolgoročne stabilnosti WiFi signalov, posledično se soočajo s težavami zaradi natančnosti nekaj dni po kalibraciji. Pogoste, drage in časovno potratne ponovne kalibracije so potrebne za reševanje teh težav. Metode, temelječe na matematičnih modelih, poskušajo eliminirati kalibracijske postopke s simulacijo širjenja signala. Večina teh metod vseeno privzame vsaj nekatere parametre propagacije kot fiksne in tako slabo naslovi variabilnost WiFi signalov in dolgoročno stabilnost. Izključno WiFi modelna metoda, ki uspešno naslovi te težave in zahteva, da mobilni terminal samo oddaja ali sprejema WiFi signale, je končni cilj WiFi metod za določanje položaja v zaprtih prostorih. Ta doktorska dizertacija predstavlja novo metodo za določanje pozicije znotraj prostorov, z glavnim ciljem, da naslovi težave pri realni uporabi. Zato smo se osredotočili na razvoj metode z natančnostjo, ki je primerljiva z najsodobnejšimi metodami, hkrati pa je cilj zmanjšati kompleksnost implementacije in vzdrževanje za dolgoročno uporabnost. Predstavljena metoda je modelnega tipa in implementira prilagodljivo delovanje, zato ne zahteva nobenega človeškega posredovanja. Dizertacija podrobno razpravlja o temah dolgoročne stabilnosti WiFi signalov, o metodah, temelječih na sprejemanju in oddajanju signalov, prihodnjih standardih WiFi, primerljivosti sorodnih metod in arhitekturnih vplivih z ozirom na realno uporabnost. Naša metoda predstavljena v tej nalogi oceni prametre propagacije signala iz poznavanja pozicije dostopnih točk, arhitekturnega načrta z informacijami o predelnih stenah in s pomočjo opazovanja moči paketov, ki potujejo med dostopnimi točkami. Iz teh podatkov se propagacijski parametri definirani v modelu določijo v realnem času. Naprava, ki želi določiti pozicijo zajame informacijo o moči paketov, ki jih pošiljajo dostopne točke. Te meritve so uporabljene v algoritmu za določanje pozicije naprave, ki teče na strežniku. Predstavljena metoda je bila primarno razvita in evalvirana v enosobni in večsobni postavitvi pisarniškega okolja. Sposobnost metode, da se enostavno prilagodi vsakemu okolju, je poudarjena z evalvacijo v dveh okoljih – pisarniškem in stanovanjskem. Med obema evalvacijama nismo spremenili nobenega parametra metode, kar indicira njeno univerzalnost. V nadaljevanju predstavimo tudi evalvacijo metode v dolgem hodniku, ker je v raziskovalnem področju lokalizacije znotraj prostorov tako okolje pogosto uporabljeno. Evalvacija predlagane metode v pisarniškem okolju je rezultirala v povprečni napaki 2,63 m in 3,22 m za enosobno in večsobno postavitev. Druga evalvacija je bila opravljena v stanovanjskem okolju, za katerega nismo spreminjali metode ali njenih parametrov. Naša metoda je tekom evalvacije štirih neodvisnih setov meritev, od katerih je vsak sestavljen iz 17 lokalizacijskih točk, dosegla povprečno napako lokalizacije 2,65 m s standardno deviacijo 1,51 m. Visoka natančnost lokalizacije ob upoštevanju zapletenega in realističnega večsobnega tlorisa, ki vsebuje več vrst sten, realistično pohištvo in motnje signalov iz sosednjih stanovanj, dokazuje uporabnost metode v praksi. Natančnost je primerljiva z najsodobnejšimi metodami, medtem ko naša metoda zahteva veliko manj zapletene postopke namestitve in/ali strojne zahteve. V drugem delu teze posplošimo WiFi metodo, da lahko uporablja tudi druge frekvence poleg 2,4 GHz WiFi. Z definicijo fuzijskega algoritma, ki upošteva natančnost posameznih frekvenc, smo definirali MFAM metodo – večfrekvenčno prilagodljivo modelno metodo za določanje lokacije znotraj stavb (ang. multiple frequency adaptive model-based indoor localization method). MFAM metoda predstavlja eno prvih modelnih metod, ki lahko hkrati uporablja več frekvenc. MFAM metoda je bila evalvirana v stanovanjskem okolju na dveh frekvenčnih pasovih: 868 MHz in 2,4 GHz. Metoda je ohranila pozitivne lastnosti predlagane WiFi metode (tj. izključno modelni pristop, prilagodljivo delovanje, možnost široke uporabe na dosegljivi strojni opremi), hkrati pa rezultira v boljši natančnosti zaradi fuzije signalov več frekvenc. Uporaba več frekvenc je izboljšala povprečno napako iz 2,65 m pri uporabi WiFi na 2,16 m, s čimer se izboljša natančnost lokalizacije za 18%podobne izboljšave smo opazili tudi pri standardnemu odklonu. Čeprav je natančnost predstavljenih WiFi in MFAM metod primerljiva, če ne boljša, kot trenutno najsodobnejše metode, je eden najpomembnejših dosežkov našega dela uporabnost metode v realnih situacijah in njena dolgoročna stabilnost. Definicija naše metode zagotavlja, da bo natančnost metode ob času postavitve enaka kot dneve kasneje brez človeške interakcije