A Method of Map Matching based on Particle Filter in Indoor Positioning

In recent years, with the rapid development of robotics, indoor mobile robots have gradually penetrated many aspects of human production and life. High accuracy localization is important for mobile robots in a variety of scenarios, including domestic and public places. In this paper, we present a fusion approach for indoor localization of a smartphone-based robot, which fuses the information of inertial sensors, encoder and indoor maps. In our approach, we first propose a dead reckoning model to obtain the position and attitude of the robot. Secondly, the information obtained by the encoder is used to identify the turning area. The difference between the turning position by our proposed algorithm and the known turning position is compared with a preset threshold. The estimated position converge on its true position. Finally, we design an Extended Kalman Filter (EKF)based fusion algorithm to fuse the inertial data, encoder data and map information. We evaluate our proposed method in an office building. The results show that the mean positioning error of the proposed method on the two routes is 0.7220m and 0.7150m respectively, which is a 60.4% and 68.3% reduction compared with the fusion of DR/Encoder approach.

Data assimilation is the process of estimating the state of dynamic systems (linear or nonlinear, Gaussian or non-Gaussian) as accurately as possible from noisy observational data. Although the Three Dimensional Variational (3D-VAR) methods, Four Dimensional Variational (4D-VAR) methods and Ensemble Kalman filter (EnKF) methods are widely used and effective for linear and Gaussian dynamics, new methods of data assimilation are required for the general situation, that is, nonlinear non-Gaussian dynamics. General Bayesian recursive estimation theory is reviewed in this thesis. The Bayesian estimation approach provides a rather general and powerful framework for handling nonlinear, non-Gaussian, as well as linear, Gaussian estimation problems. Despite a general solution to the nonlinear estimation problem, there is no closed-form solution in the general case. Therefore, approximate techniques have to be employed. In this thesis, the sequential Monte Carlo (SMC) methods, commonly referred to as the particle filter, is presented to tackle non-linear, non-Gaussian estimation problems. In this thesis, we use the SMC methods only for the nonlinear state estimation problem, however, it can also be used for the nonlinear parameter estimation problem. In order to demonstrate the new methods in the general nonlinear non-Gaussian case, we compare Sequential Monte Carlo (SMC) methods with the Ensemble Kalman Filter (EnKF) by performing data assimilation in nonlinear and non-Gaussian dynamic systems. The models used in this study are referred to as state-space models. The Lorenz 1963 and 1966 models serve as test beds for examining the properties of these assimilation methods when used in highly nonlinear dynamics. The application of Sequential Monte Carlo methods to different fixed parameters in dynamic models is considered. Four different scenarios in the Lorenz 1063 [sic] model and three different scenarios in the Lorenz 1996 model are designed in this study for both the SMC methods and EnKF method with different filter siz

Navigation is one of the most essential human needs. People need to know their location anywhere and everywhere, indoors and outdoors, for example for tracking children and older people who are in extreme need of care or for providing a precise indoor location of calls to 911. The latter is especially important since more than seventy percent of calls are generated indoors. Other important fields are tracking of personnel in possibly dangerous environment, LBS/LBA applications, and social networking that rely on indoor localization. Unfortunately, broadly used satellite navigation receivers work perfectly only under open sky. Operating indoors, GPS/GNSS receivers suffer from signal attenuation when satellite signals propagate through a roof and walls of a building and from the multipath due to their reflection. As a result, accurate GNSS-based position fix is almost impossible in most indoor conditions. We present Coursa Venue solution that was developed by TDK-Invensense for infrastructure-less indoor positioning on commercial smartphones. The solution consists of two major parts: cloud-based software for fingerprinting and mobile applications for Android and iOS that provide real-time blue dot positions to users. TDK-Invensense's approach to real-time indoor positioning is based on fusion of multiple technologies. Measurements from such smartphone sensors as IMU (3D accelerometer, gyroscope), a magnetic field sensor (3D magnetometer), WiFi and BLE modules are used for hybrid indoor positioning in the navigation engine. Particle filtering is used as the fusion engine. Indoor navigation software uses such technologies as PDR, geomagnetic fingerprinting, Wi-Fi/BLE fingerprinting, and, optionally, map matching. TDK-Invensense' ‘s PDR provides prediction of user relative movement regardless of orientation and misalignment of a smartphone, whereas magnetic and radio fingerprinting serves for correction of inertial sensors error. The cloud-based component can create magnetic and radio fingerprint databases using either data collected by designated surveyors who walked inside a venue by predetermined routes, or crowdsourced data from users of a real-time mobile application collected during their everyday activity. This paper discusses the architecture of the Coursa Venue solution and demonstrates its positioning results in several venues with comparison to ground truth paths to provide statistical assessment and key performance indices.

User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.

Indoor localization is a fundamental issue in IoT (Internet of Things). On the other hand, IoT provides a lot of networked devices that would help increase the precision of indoor localization. Particle Filter (PF) is widely used in indoor localization due to its flexibility that can adapt to different, and usually complex, indoor floorplans and furniture placements. In this work, we consider the fusion of multi-sensory data using PF. We focus on three types of popular sensors: IM (inertial measurement) sensor, RF (radio frequency) sensor, and environmental visual sensor. In particular, with environmental visual sensors, there is no extra device to be attached to localized targets. We propose a PF model that can adopt these types of sensory inputs. We show that in scenarios where visual sensory inputs are available, sub-meter precision can be achieved and in places with no visual coverage, seamless localization with reasonable precision can be supported by other sensors. Field trial results are presented, which show that our model is quite suitable for areas like lobby, corridor, and meeting room.

This paper presents a method of map matching that is a scheme for accurate guidance of an electric wheel chair. Indoor navigation seems to require more accurate guidance than outdoor areas, the location estimation by the rotary encoders embedded in the wheel chair cannot be satisfy the accuracy requirement, because estimation error is accumulated as the wheel chair travels. The authors propose the map matching that uses the building structure in order to compensate for accumulated position error. The corner detection and its position information are used for replacement as a correct position. The methods of the corner detection and the calculation model for the correct position are shown, and the validity of the proposed methods are confirmed by the experiment using a laser range finder.

With the development of indoor positioning methods, such as Wi-Fi positioning, geomagnetic sensor positioning, Ultra-Wideband positioning, and pedestrian dead reckoning, the area of location-based services (LBS) is expanding from outdoor to indoor spaces. LBS refers to the geographic location information of moving objects to provide the desired services. Most Wi-Fi-based indoor positioning methods provide two-dimensional (2D) or three-dimensional (3D) coordinates in 1–5 m of accuracy on average approximately. However, many applications of indoor LBS are targeted to specific spaces such as rooms, corridors, stairs, etc. Thus, they require determining a service space from a coordinate in indoor spaces. In this paper, we propose a map matching method to assign an indoor position to a unit space a subdivision of an indoor space, called USMM (Unit Space Map Matching). Map matching is a commonly used localization improvement method that utilizes spatial constraints. We consider the topological information between unit spaces and moving objects’ probabilistic properties, compared to existing room-level mappings based on sensor signals, especially received signal strength-based fingerprinting. The proposed method has the advantage of calculating the probability even if there is only one input trajectory. Last, we analyze the accuracy and performance of the proposed USMM methods by extensive experiments in real and synthetic environments. The experimental results show that our methods bring a significant improvement when the accuracy level of indoor positioning is low. In experiments, the room-level location accuracy improves by almost 30% and 23% with real and synthetic data, respectively. We conclude that USMM methods are helpful to correct valid room-level locations from given positioning locations.

The article presents an indoor localization scheme for mobile devices based on GSM Received Signal Strength fingerprints combined with embedded sensor information and an area site map. Displacements of a mobile user are first estimated using a sensor dead-reckoning approach that adapts stride length to different users and environments, and a dynamically switched orientation estimation scheme responding to orientation changes of the mobile device. Positions derived from GSM fingerprints, along with constraints imposed by a site map, are then integrated using a particle filter in order to prevent the accumulation of dead-reckoning errors over time. The study demonstrates that a standard handset with cellular network access and embedded inertial sensors can provide a good solution for indoor localization.

Map matching is the process of finding a match for each GPS point in a vehicle's trajectory to roads on a digital map. Extensive research has been conducted during the last years yielding many algorithms based on different approaches. One of the challenges that face those algorithms is the interruption of GPS signals that occurs specially in dense urban environments. In these cases on-board sensors like odometers and accelerometers can be used temporarily for positioning, however due to the poor accuracy of these methods, the quality of map matching decreases significantly. In this paper, we propose a method that improves the quality of map matching when GPS signals are not available. This method is based on a particle filter using heading and velocity measurement. We evaluate this method through its integration with an existing topological map matching algorithm. We compare the performances when this algorithm is used alone and when associated with the particle filter.

The paper answers a fundamental question: can the accuracy of Wi-Fi based localization be significantly increased by fusing information from alternate sources like LTE signals and magnetometers, collected through software defined radios and smart-devices? Further, it aims to eliminate dependency of well established localization techniques on only Wi-Fi access point (AP) positions, and instead, proposes a diversity-leveraging architecture called as the Wireless Locator (Wi-LO) framework. Wi-LO is a client-server paradigm for indoor localization that achieves precision by pattern-matching the collected signal samples with a priori references for each transmitter type, thus giving a stand-alone decision for these diverse sensing modes. The novelty of the paper is fusing these decision outcomes and resolving mis-matches (for e.g., Wi-Fi and LTE suggest different locations) in a seamless manner, and also identifying the best source to use at each location based on its spatial resolution. Wi-Lo is rigorously evaluated on a test-bed, with the proposed scheme of combining Wi-Fi, LTE and magnetometer performing better localization than the classical Wi-Fi-only approach in both urban (+8%) and rural (+25%) scenarios.

Indoor localization can provide a number of different services such as location-aware advertisement, indoor navigation and automating different appliances based on the user location. A number of different techniques such as time-difference- of-arrival, angle-of-arrival, time-of-flight, and received signal strength indicator (RSSI) have been used to provide Location Based Services (LBS). RSSI is one of the widely used methods as it is cost efficient and easy to implement. However, RSSI's performance is limited by multipath fading and indoor noise. Particle Filter (PF) is an accurate Bayesian Filtering algorithm that can improve the performance of RSSI-based indoor localization. However, PF is not able to satisfy the high accuracy requirement (possibly 10cm) of indoor localization. In this paper, we present Particle Filter-Extended Kalman Filter (PFEKF) cascaded algorithm that combines PF and EKF in series to reduce the impact of multipath effects and noise on the RSSI. Our experimental results show that PFEKF improves the localization accuracy by 31.3% and 33.9% in 3D and 2D environments respectively when compared with using only a PF.

<h3>Abstract</h3> Filtering-based indoor positioning using ultra wideband (UWB) requires known velocity to predict prior position in the prediction stage. Velocity can be obtained from an inertial measurement unit (IMU) sensor or the posterior state vector at the previous time stamp. Both methods have limitations when using them in practice. This paper proposes two novel velocity determination approaches, which use measurements to approximate velocity in a self-contained way. They are integrated into particle filtering algorithms for prior position determination. The test result shows that the particle filter with the proposed approaches performs similarly to the Rao-Blackwellized particle filter and slightly better than the particle filter with IMU. Compared with the standard particle filter, the particle filters with our proposed approaches achieve similar positioning accuracies with less computation time. Moreover, it is found that the integration of Angle-of-Arrival measurements in particle-filter-based positioning improves the 3-D positioning accuracy by about 37.3% on average.

liuyujiao2005@126.com Abstract Aiming at a nonlinear/non-Gaussian filter problem, the data processing of a single-yarn strength testing system, a filtering method based on the particle filter algorithm is put forward. This paper expounds the principle, and the working process and the procedures of particle filter. It discusses in detail the application of particle filter in the single-yarn strength testing, the modeling of testing system state equation, and the implementation of this algorithm in a project. It also indicates that particle filter algorithm used to process single-yarn strength test data achieves a good effect and satisfactory filtering precision by simulated tests. Finally, this paper predicts that particle filter will play an important role in general data processing and analyzing. The detection of the yarn quality is very important for cotton production. Yarn strength is the reflection of inner quality of yarn and is the necessary condition of yarn's processing performance and end-use. The unit of single-yarn strength is Newton (N) per centimeter (CN). By stretching testing with a single-yarn strength tester, the yarn's characteristic parameters will be obtained, and the physical index of breaking strength, breaking elongation, fracture strength, fracture time and strength tensile curves can be determined, so the quality of yarn will be ascertained (1) . This paper majors in the research of the parameter of the single-yarn strength of yarn. The strength of yarn will increase with increasing within a certain twist; however the strength of yarn will decrease when the twist exceeds the critical value. The relationship between yarn elongation and tension is often nonlinear. When the yarn quality is tested by single-yarn strength tester, the interference noise of test environment in the industrial field is non-Gaussian. So the processing of single-yarn strength testing data is a typical nonlinear and non-Gaussian filtering problem. The traditional digital filtering methods normally only filter out the caused by single reason and can't achieve satisfactory results for complex noise. In order to solve this problem, a large number of nonlinear recursive filtering algorithm such as extended Kalman filtering (EKF), the modified gain Kalman filter (MGEKF), U Kalman filter (UKF) algorithm come forth in recent years, and these filters are based on particular assumption to ensure the optimality. The actual data is usually very complex, and contains many factors such as non-Gaussian, nonlinear, high dimensional and noise; in this case, the Kalman filter generally can't get the analytical solution. This is a common problem and appears in many different kinds of fields (2-11) . Now, a new sequential Monte Carlo particle filter algorithm based on Bayesian principles has been paid more attention. The particle filter technology realize the recursive Bayesian filter with non- parametric Monte Carlo simulation method, which can fit to any state space model and the nonlinear systems which can't be expressed by traditional Kalman filter, and its accuracy approach the accuracy of optimal estimation (12) . This paper places emphasis on introducing the principle and method of the particle filter, and the application of the nonlinear data processing.

In this paper, Monte Carlo Simulation (MCS) method was used as an uncertainty assessment tool for air flow, net thrust measurement. Uuncertainty sources of the net thrust measurement were analyzed, and the probability distribution characteristics of each source were discussed. Detailed MCS methodology was described including the effect of the number of simulation. Compared to the conventional sensitivity coefficient method, the MCS method has advantage in the uncertainty assessment. The MCS is comparatively simple, convenient and accurate, especially for complex or nonlinear measurement modeling equations. The uncertainty assessment result by MCS was compared with that of the conventional sensitivity coefficient method, and each method gave different result. The uncertainties in the net thrust measurement by the MCS and the conventional sensitivity coefficient method were 0.906% and 1.209%, respectively. It was concluded that the first order Taylor expansion in the conventional sensitivity coefficient method and the nonlinearity of model equation caused the difference. It was noted that the uncertainty assessment method should be selected carefully according to the mathematical characteristics of the model equation of the measurement.

We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches constructed primarily to account for model misspecification. We prove finite sample concentration bounds for estimators derived from the proposed method along with a linear tempering extension and apply these results to a realistic setting where a computer model is misspecified. We then illustrate the SMC method for inference in a modular chemical reactor example that includes submodels for reaction kinetics, turbulence, mass transfer, and diffusion. The samples obtained are commensurate with a direct-sampling approach that consists of running multiple Markov chains, with computational efficiency gains using the SMC method. Overall, the SMC method presented yields a novel, rigorous approach to computing with cut-Bayesian posterior distributions.