A multiclassifier approach for topology-based WiFi indoor localization

Robot odor source localization in indoor environments based on gradient adaptive extremum seeking search

As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3∼4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

The purpose of this paper is to provide a new method for solving the problem of the 3D source localization in indoor environments with weak airflow by the experimental study on the multi-robot method. For this purpose, we developed a 3D MRO (mobile robot olfaction) system consisting of three mobile robots. The gas sensor carried with each robot has a moving range of 0.5 m to 1.5 m on the Z-axis. A total of 60 experiments were conducted to validate and compare the standard whale optimization algorithm (SWOA) and the standard particle swarm optimization (SPSO) methods in a 7.65 m×4.1 m experimental area with two source heights (0.75 m and 1.05 m). For two source heights, the success rate and localization step of the SPSO method are 56.7% (17/30) and 31.4 steps, respectively. In addition, the success rate and localization step of the SWOA method are 80% (24/30) and 32.9 steps, respectively. These results show that the SWOA method has strong application potential in indoor environments with weak airflow.

This paper researches a robotic localization system based on motion capture indoor environment. We apply motion capture technology to realize multi-robot positioning which achieves the multi-robots’ localization in indoor environment. It includes motion capture system, some robots, and main computer for data calibration and communication. Motions capture system based on the theory of photogrammetry which was analyzed. And the virtual experiment environment was established according to the actual experimental environment. The performance is demonstrated by numerical simulation and experiment.

Global localization problem is one of the classical and important problems in mobile robot. In this paper, we present an approach to solve robot global localization in indoor environments with grid map. It combines Hough Scan Matching (HSM) and grid localization method to get the initial knowledge of robot's pose quickly. For pose tracking, a scan matching technique called Iterative Closest Point (ICP) is used to amend the robot motion model, this can drastically decreases the uncertainty about the robot's pose in prediction step. Then accurate proposal distribution taking into account recent observation is introduced into particle filters to recover the best estimate of robot trajectories, which seriously reduces number of particles for pose tracking. The proposed approach can globally localize mobile robot fast and accurately. Experiment results carried out with robot data in indoor environments demonstrates the effectiveness of the proposed approach.

BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images

How to filter fluctuant RSSI signal has always been a difficult problem in an indoor localization system. This paper provides an efficient indoor localization algorithm in a BLE5.0 based scan-broadcast network by building RSSI path-loss model without a great deal of fingerprints. This method builds a RSSI-Distance fading model between one position node (PN) and one markup node (MN) by maximum likelihood estimation (MLE) based on Gauss distribution of RSSI data. Then the rough fading model about RSSI in data collecting intervals of 1 m will be get. In this paper we reduce the distance intervals in 0.1 m by fitting of path loss model and making discrete samples of confidence intervals to improve the accuracy of localization. Finally, the whole fading regularly will be fixed and the location errors of PN will be determined by centroid model (CM). The results show that sampling interval with high precision can benefit the accuracy performance in an indoor localization environment.

New algorithm for footstep localization using seismic sensors in an indoor environment

The indoor positioning technology is based on the hotpots of location based services (LBS). However, most indoor positioning systems are two-dimensional and couldn’t meet the requirements of today’s LBS. The complex indoor structures and environment determine the floor positioning rather than the altitude positioning in the vertical direction, so the floor identification is the key to three-dimensional indoor positioning systems. There are many restrictions for the existing floor identification methods based on barometer or inertial sensor. They need to get the comparable data in advance, or detect the test data changes in a certain period of time for accurate identification. The current floor identification methods based on ordinary Wi-Fi fingerprints are less effective in the complex environment. Therefore, a new floor identification method based on confidence interval of Wi-Fi signals was developed in this paper, which was divided into the offline stage and the online stage. In the offline stage, the dynamic Wi-Fi signal sequences were collected fast. Then, the adaptive partitioning of Wi-Fi signal intervals was carried out according to RSSI distribution characteristics in the multi-floor environment. Finally, the confidence levels were calculated and the database of fingerprints was constructed. In the online stage, the matching between the test fingerprints and those in the database was applied to obtain the confidence of APs on each floor monitored by the test fingerprints. The sums of the confidence of APs on each floor were calculated, and the floor corresponding to the maximum value was judged as the target floor. To verify the performance of the proposed method, it was compared with the majority voting committees, K-means, Naive Bayes and KNN methods. The results indicate that it was better than other methods in large complex indoor scenes. Its identification accuracy rate was 92.2% and the error rate was 7.8% only one floor away. Moreover, it also could significantly reduce the size of the fingerprint database and further improve the efficiency of algorithm.

Although research community have put lots of effort in improving localization in indoor environment through various of sensing techniques, outdoor localization still heavily relies on GPS solely. Despite GPS performs successfully in perfect outdoor scenario (e.g., large open air zone), many other outdoor scenarios in metropolitan area make GPS fail to achieve a satisfactory performance due to signal blocking or multi-path effect. Therefore, to improve localization performance in metropolitan area, an integrated outdoor localization solution is desired, which takes GPS as one part and other sources as supplementary. For designing such an integrated solution, we need to consider two questions: (1) what are the proper supplementary sources? (2) when are the proper moments to trigger a switch between GPS and other sources? To find a proper complementary source for outdoor localization in urban area, we have to consider the potential large-scale infrastructure and human effort in collecting and labelling data. The ever-expanding scale of WiFi deployments in urban area has made accurate GPS-free outdoor localization become possible. Additionally, there are many existing crowdsensing database available that collect WiFi signal of users' surrounding hotspots and users' locations. Leveraging the existing infrastructure and data source, we propose WOLoc (WiFi-only Outdoor Localization) as an outdoor localization solution using only WiFi hotspots labelled by crowdsensing. On one hand, we do not take these labels as fingerprints as it is almost impossible to extend indoor localization mechanisms by fingerprinting urban areas. On the other hand, we avoid the over-simplified local synthesis methods (e.g., centroid) that significantly lose the information contained in the labels. Instead, WOLoc adopts a semi-supervised manifold learning approach that accommodates all the labeled and unlabeled data for a given area, and the output concerning the unlabeled part will become the estimated locations for both unknown users and unknown WiFi hotspots. Empirical results show that WOLoc can achieve a meter-level localization accuracy in many urban areas. Despite the success of WOLoc in many cases, due to the lack of labelled data for hotspot location, WOLoc may suffer from an imbalanced alignment between the user manifold and hotspot manifold. Such misalignment may lead to extreme cases which are meaningless in some scenarios. To reduce such failures, we apply text mining techniques to analyze the SSIDs of hotspots, and develop a pipeline to incorporate SSID analysis in both pre-processing and hotspot labelling process of WOLoc. We make use of existing crowdsourced location services databases (e.g. Foursquare, Google Places) to give more accurate hotspot location labels. Empirical results show that the hotspot SSID analysis pipeline has successfully reduced extreme failure cases for WOLoc and lead it to a more consistent performance To find a proper trigger between GPS and WiFi, we need to understand how GPS performs in different urban outdoor scenarios. The latest developments in Android have granted us access to raw GNSS measurements, which contain far more information than commonly derived GPS location indicators. We explore these newly available measurements in order to better characterize the diversified urban scenarios and propose a deep learning model to identify representations for respective location contexts. With the deep profiling, we offer a more fine-grained semantic classification than binary indoor-outdoor detection and derive a GPS error indicator more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations. To sum up, in this dissertation, we propose a series of data-driven approaches that aim for an integrated outdoor localization by exploiting WiFi RSSI data, WiFi SSID labels and GNSS raw measurements. We choose WiFi as supplementary sources and design a full version of system based on manifold learning technique to achieve WiFi-only outdoor localization in urban areas. We further develop the SSID-based text-mining techniques to improve both accuracy and robustness. We finally propose a GNSS profiling technique to evaluate GPS performance and differentiate different location context, which can be used as a trigger between GPS and WiFi in an integrated solution.

Experimental study on 3D source localization in indoor environments with weak airflow based on two bionic swarm intelligence algorithms

Radio Frequency Identification (RFID) is an information exchange technology based on radio wave communication. It is also a possible solution to indoor localisation. Owing to multipath propagation and anisotropic interference in the indoor environment, theoretical propagation models are generally not sufficient for RFID-based localisation. In fact, the received radio frequency signal distribution may not even be monotonic and this makes range-based localisation algorithms less accurate. On the other hand, range-free localisation algorithms, such as k Nearest-Neighbour (kNN), require reference tags to be spread throughout the whole three-dimensional (3D) space which is frequently not practical. In this work, a hybrid real-time localisation algorithm that combines reference tags with Received Signal Strength Indicator (RSSI) ranging is introduced to improve RFID-based 3D localisation in indoor environments. The localisation algorithm is implemented in MATLAB and is synchronised with radio signal data in real-time. Results show that the proposed hybrid algorithm achieves an average 3D localisation error of 1.08 m which represents a significant improvement over algorithms that use only kNN or RSSI.

For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.

This paper presents a new object entity based global localization approach with stereo camera. A local invariant feature and stereo depth information are used as visual features. The map we use here is a hybrid of global topological map and local object location map. The topological map includes some semantic information about the representing space and the object entities in the space. The object location map has the pose information of each object entity and visual features for object recognition. The localization process consists of two stages: coarse pose estimation and refined pose estimation. The coarse pose is computed by using the object recognition and point cloud fitting method. And the refined pose is estimated with particle filtering algorithm. An experiment shows that our approach can be an effective vision-based global localization method.

In this chapter, we explain how multi-objective genetic algorithms can be applied to the design of fuzzy rule-based systems for pattern classification problems. For a multi-class pattern classification problem with many continuous attributes (e.g., wine classification with 13 continuous attributes [1]), a fuzzy rule-based classification system is designed by a multi-objective genetic algorithm [2, 3] with two objectives: to minimize the size of the fuzzy rule-based classification system and to maximize its performance [4, 5]. The size of the fuzzy rule-based classification system is expressed by the number of fuzzy if-then rules, and its performance is measured by the number of correctly classified training patterns. In our approach to the design of the fuzzy rule-based classification system, first a large number of fuzzy if-then rules are generated from training patterns as candidate rules for the rule selection. Then a small number of fuzzy if-then rules are selected from the candidate rules by the two-objective genetic algorithm. The two-objective genetic algorithm tries to find all the non-dominated solutions (i.e., non-dominated rule sets) of the rule selection problem with the above-mentioned objectives. One difficulty of the rule selection method for high-dimensional pattern classification problems with many continuous attributes is that the number of candidate fuzzy if-then rules becomes intractably large. For example, the number of candidate rules is 613 ≅ 1.3 × 1010 for the wine classification problem with 13 continuous attributes if we have six fuzzy sets (terms) on each axis of the 13-dimensional pattern space.