Coarse-to-fine robot localization method using radio fingerprint and Particle Filter

In this paper, we propose a surface correlation based fingerprinting method in sparse Wi-Fi environment. The proposed method can estimate location even with very few Wi-Fi APs by using accumulated spatial RSS pattern. We verify performance of the proposed method through several tests in a building with complex paths. Many studies have developed localization technologies that use measurements such as RSS (Received Signal Strength) or RTT (Round Trip Time) for Wi-Fi signal. All RF have signal noise and distortion problems as well as multipath problem in indoor environments. As the distance from the RF signal source increases, the discrimination of the RSS decreases due to RF signal noise and distortion. Therefore, ambiguity of RSS increases as the distance increases, the performance deteriorates. The conventional RF based localization method have tried to maintain dense RF environment to guarantee stable and accurate performance. However, in order to maintain the dense RF environment, it is becoming a barrier to commercialization of localization technology due to high cost of constructing RF infrastructure and maintenance difficulty. In this paper, we propose a Surface Correlation (SC) based fingerprinting technique to maintain high positioning performance even with few Wi-Fi APs. The surface is the accumulated RSS pattern data during the movement of a pedestrian, which means the spatial RSS pattern. Conventional fingerprinting methods using kNN (k-Nearest Neighbors) and WkNN (Weighted kNN) are very vulnerable to signal noise and distortion because they utilize instantaneous RSS data. The performance of Particle Filter (PF) based localization technology is also degraded in areas where Wi-Fi AP is sparsely distributed. However, the proposed method is very robust against signal noise and distortion because it compares the spatial RSS pattern with the reference RSS pattern on the fingerprinting database. This increases localization accuracy and availability for Wi-Fi coverage. For localization, the proposed method conduct correlation process for the spatial RSS pattern of a pedestrian with reference RSS pattern of database. So, the reference RSS pattern should be extracted from a reference trajectory that matches a pedestrian`s walking path. The proposed method utilize a database of ‘link-node’ structure to dynamically generate a reference trajectory in indoor environment with complicated path. It generate candidate reference trajectories using only MAC address and turn event of Pedestrian Dead-Reckoning (PDR). The proposed method fix a reference trajectory between these candidate trajectories through correlation process of measured RSS pattern with each reference RSS pattern. A reference point on the fixed reference having most similar RSS pattern to a pedestrian`s is considered as a current location. In order to improve performance at a fork where RSS ambiguity increases, the proposed method estimates preferentially for a reference trajectory matching with PDR turn event. The turn event can be wrong due to various motion of a pedestrian. The proposed method selects a correct reference trajectory by continuously performing correlation process on candidate reference trajectories. In order to verify the performance of the proposed surface correlation based fingerprinting method, we conducted several tests in a three-story building. Before the tests, we conducted a survey of Wi-Fi environment to construct fingerprinting database in the building. As a result of the survey, 170 APs are distributed in the building. We reduced the number of APs by 20% from 170 to 17, and analyzed performance of the proposed and conventional localization methods. The test results show that the performance of the conventional localization methods degrade as the number of applied APs decreases. The proposed fingerprinting method shows a 3 meter localization error even with very few Wi-Fi APs, and unlike the conventional fingerprinting method, the localization error is bounded to be within 5m due to the use of spatial RSS pattern. In this paper, we presented the fingerprinting method using the surface correlation in sparse Wi-Fi environment. The proposed fingerprinting method accumulates Wi-Fi RSS during movement of a pedestrian. The proposed method compares the spatial RSS pattern, which is accumulated RSS data, with reference RSS patterns of fingerprinting database. By comparing the RSS patterns, the proposed method greatly improves accuracy as well as stability. In addition, it can be effectively applied in a sparse Wi-Fi environment by improving the availability of ambiguous areas of Wi-Fi coverage. Through these results, we expect to be able to provide accurate and reliable location information even in indoor weak RF environment.

This paper presents a positioning method using range-based Bluetooth RSSI data and odometry data for a mobile robot. These days, Bluetooth beacons are widely used for various applications which are based on both location and environmental information. There are two localization methods: a range-free method called the ‘fingerprint method’ and a range-based method called the ‘triangulation method’. Because of the Bluetooth RSSI resolution, most researches are using the fingerprint method. In this paper, the localization method, which uses odometry data and range-based Bluetooth RSSI data, is proposed. The range data from Bluetooth RSSI is directly used without triangulation. Also, the particle filter is adopted to combine the odometry data with the range data from Bluetooth RSSI. The performance of the proposed method is evaluated by simulations and experiments.

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

A key problem of mobile robots is that of the positioning capability in order to detect their locations in the operating environment. A promising technology used in indoor localization recently is Visible Light Communication (VLC). In this paper, an integrated Angle of Arrival-Received Signal Strength (AOA-RSS) localization method using the VLC, which could get high accuracy with simple hardware, is proposed. In addition, the Extended Kalman Filter (EKF) and Particle Filter (PF) algorithms are combined with the proposed AOA-RSS localization method in order to achieve higher accuracy of the mobile robot positioning. By computer simulation, the performance of the proposed AOA-RSS localization method combined with the EKF and PF algorithms are compared. The combination scheme outperforms the individual VLC localization with small error approximation of a few centimetres.

This paper develops a parallel implementation of the ensemble Kalman filter (EnKF) based on shrinkage covariancematrix estimation. The EnKF is a sequential Monte Carlo method for parameter and state estimation of highly nonlinearmodels. In the context of ensemble based methods, background error correlations are estimated via anensemble of model realizations. In practice, the model dimension (number of model components) is several timeslarger than the ensemble size owing to the computational effort involved in one single model propagation. Thisconstraint yields to spurious correlations (correlation between distant model components in space) in the backgrounderror estimations. Commonly, localization methods are utilized in order to reduce the impact of spuriouscorrelations. For instance, correlations of model components are reduced based on some function of their physicaldistances. Some EnKF implementations are formulated in such manner that, localization methods are implicitlyinvolved in the assimilation of observations. In this context, one of the best ensemble Kalman filter implementationsis the local ensemble transform Kalman filter (LETKF). In the LETKF, each model component is surrounded by alocal box of size r and then, the information contained within the local domain (i.e., observed components) is used toperform the assimilation. However, for sparse observational networks, r can become sufficiently large with respectto the ensemble size and therefore, the local analysis corrections can be impacted by spurious correlations. Wepropose an ensemble Kalman filter implementation in which the background error covariance matrix is estimated viathe Rao-Blackwell Ledoit and Wolf (RBLW) estimator. This estimator has proven to be better conditioned than thetrue (background) error covariance matrix. In addition, the explicit computation of the RBLW estimator is notrequired in our formulation. This implies enormous savings in terms of memory considering the typical order ofbackground error covariance matrices O(10e8) Furthermore, the proposed EnKF implementation can be efficientlyperformed in parallel: each model component is surrounded by a local box, a local background error covariancematrix is estimated based on the RBLW estimator and then, the ensemble Kalman filter equations are locally used inorder to compute the local analysis corrections. After the local assimilation, each model component is mapped backonto the full model domain from where the global analysis solution is obtained. In operational data assimilation, EnKF implementations are attractive owing to their potential for parallelization. Experiments are performed making use of the Atmospheric General Circulation Model SPEEDY. The proposed method is coded in FORTRAN and MPI is used for the local assimilation of observations. The predicted variables are the zonal and meridional wind components, the temperature and the specific humidity. For each model variable, 8 layers with the T-63 resolution (192 x 96 grid points) are considered which provide a total number of 589,824 model components. The ensemble size is 94. The number of processors utilized are 96, 256, 512, 768, 1024 and 1536. The results reveal that, the use of the RBLW estimator can mitigate the impact of spurious correlations under sparse observational networks and even more, for different number of processors, the computational effort of the proposed EnKF implementation is comparable to that of the LETKF where no covariance estimation is performed. In terms of accuracy, the proposed implementation performs better than the LETKF formulation in the Root Mean Square Error sense.

One of the fundamental tasks in robotics is the creation of a map of the area where the robot is moving. The created map is used for its navigation in this environment. This task is called Simultaneous Localisation and Mapping (SLAM). There are many various algorithms for accomplishing this task, such as Kalman filters or particle filters. At present, mainly the particle filters are used. Together with grid-based maps, they allow the creation of the most precise and consistent maps.

Localization is one of many issues in mobile robot study. Localization is an essential ability for mobile robot to determine its location, so that it can plan a movement and go to a desired location. The mutual method for mobile robot localization is using a particle filter. High computation needs in particle filter is one of problem in particle filtering to get accurate location. The paper proposes a low computational mobile robot localization using a particle filter. It uses two methods: local localization using a dead reckoning method and global localization using a landmark-based vision sensor. Simulation results show that the proposed method provides a good estimation on the mobile robot position and orientation.

This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location database. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the LVSM is fast, but not accurate enough, whereas localization from the location database using a voting algorithm is relatively slow, but more accurate. The integration of coarse and fine stages makes fast and reliable localization possible. If necessary, the localization result can be verified by epipolar geometry between the representative view in the database and the view to be localized. In addition, the localization system recovers the position of the camera by essential matrix decomposition. The localization system has been tested in indoor and outdoor environments. The results show that our approach is efficient and reliable.

Particle Filter (PF) is considered as an extension of the Kalman filter to deal with non-Gaussian non-linear dynamic systems. The key idea is to construct a posterior probability by a set of hypotheses representing a potential state of the system. Sample impoverishment is the flaw of the PF techniques in the case of noisy observations. Recently, evolutionary algorithms are added to PF based methods to improve their functionality in noisy environments. A new PF based method called Adaptive Velocity Particle Filter (AVPF) is introduced to overcome this flaw of the PF. The experimental results show that the proposed method has a superior performance in relation to other evolutionary PF in cases of noisy observations.

This paper presents an hybridization of particle filter and local search algorithms, called local search particle filter (LSPF), and its application to human-computer interaction. The proposed algorithm combines both sequential Monte Carlo (particle filter - PF) and local search methods to achieve an accurate real-time hand tracking. The system allows to control different mouse actions through a reduced set of hand movements and gestures. Hand are segmented using a skin-color model based on explicit RGB region definition. The proposed hybrid tracking method increases the performance of general particle filter. It also improves the quality of the hand tracking task (the standard deviation between hand spatial positions for LSPF is reduced a 75% with respect to the PF algorithm). More precisely, a local search enhances a hand-simulated mouse cursor to smoothly move and thus recognize gestures for performing their associate actions.

The fundamental task in an autonomous vehicle navigation system is localization from the available sensor measurements. GPS in the vehicles locates it with error of 1 to 10 meters so localization process should be performed to avoid fatal accidents. The realization of algorithms to estimate our vehicle’s position precisely is Localization. Odometry, Kalman Filter, Particle Filter and SLAM(Simultaneous Localization And Mapping) are the techniques used in an autonomous vehicle to localize itself in the map. Among these the particle filter is widely employed in the localization of autonomous vehicles as it provides accurate position of the vehicle in the environment. This paper aims at a localization technique for autonomous vehicles or robots using Particle Filter algorithm. The position estimator is implemented using the GPS and IMU sensor measurements. The map contains specific landmarks identified such as buildings and poles which assist the vehicle to know its position accurately by matching the distance between them in the particle filtering process. The results show that this algorithm can deliver accurate vehicle positioning even in erroneous GPS data.

To address the problems of particle degradation, localization failure, and sensor noise in mobile robot localization algorithms, Robust Monte Carlo Localization is proposed in this paper. It is an enhanced approach combining adaptive odometry motion model and adaptive relocation detection. The approach incorporates velocity factors and environmental variation factors to optimize the particle filter model. Then, the result is used as initial value, and a precise result is obtained based on scan-matching. The accurately matched result is then reintroduced into the particle filter as particles. Finally, the decision for re-localization is made based on the results of precise matching and the particle distribution. The simulated experiments were conducted to evaluate the performance of our algorithm. The real-world experiments are also conducted employing a sub-millimeter high-precision instrument. The experimental results demonstrate that compared with the mainstream 2D laser-based localization methods, the accuracy of our algorithm is improved by more than 29.97%, and the failure rate of positioning in the real environment is below 10%. Thus, our algorithm substantially enhances accuracy for complex indoor positioning.

WiFi-based localization methods suffer from multipath problem in indoor environments, which leads to poor accuracy. Light Detection And Ranging(LiDAR)-based localization methods can have good accuracy. However, they are not feasible in simple and repetitive scenarios as it is difficult for scene feature extraction and matching. Therefore, a novel localization method to fuse WiFi, LiDAR and Map by integrating them into a Kalman filter framework is proposed. In this framework, the state of the filter is defined as the current and historical position sequence of the robot. The observation consists of two parts. The first is the WiFi fingerprint localization results based on the proposed distance-weighted WiFi fingerprint matching method on multi-loop segmentation map; The second part comes from the high-precision relative localization results (such as lateral localization) by LiDAR in a single repeated scene. By utilizing the priori reference position in the scene map, such lateral positioning result can be integrated with the map to formulate linear constraints on the robot position. Finally, the Kalman filter is applied to accurate localization of the robot. The proposed algorithm is verified in two scenarios, where 2D and 3D LiDAR are applied. Experimental results show the average localization error of the proposed algorithm can be reduced by 70%～80%, which demonstrate that proposed method can improve the localization accuracy and stability.

Monitoring the variables of real world dynamical systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding probability distribution over the unobserved variables. However, they suffer from the curse of dimensionality problem: the necessary number of particles grows exponentially with the dimensionality of the hidden state space. The problem is aggravated when the initial distribution of the variables is not well known, as happens in global localization problems. In this paper we present two new adaptive sampling mechanisms for PFs for systems whose variable dependencies can be factored into a Dynamic Bayesian Network. The novel PFs, developed over the proposed sampling mechanisms, exploit the strengths of other existing PFs. Their adaptive mechanisms 1) modify or establish probabilistic links among the subspaces of hidden variables that are independently explored to build particles consistent with the current measurements and past history, and 2) tune the performance of the new PFs toward the behaviors of several existing PFs. We demonstrate their performance on some complex dynamical system estimation problems, showing that our methods successfully localize and track hidden states, and outperform some of the existing PFs.

A novel infrared moving object tracking method based on particle filter and Mean Shift algorithm was presented.Firstly,it utilized the intensity distribution to represent infrared object,and constructed the observation probability model by statistical histogram.Then,Mean Shift algorithm was incorporated into the propagating process of particle filter,which induced particles distributing within the local area of observation.Compared to the conventional particle filter,the proposed method used much fewer particles to maintain the multi-mode distribution,and overcame the degeneration problem effectively.Experimental results on sequential images show that our method can track steadily when the object moves fast or is occluded,the overall performance of the proposed method is better than traditional particle filter algorithm.