Localization In Indoor Environments Research Articles

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

Abstract In this study, we consider the use of seismic sensors for footstep localization in indoor environments. A popular strategy of localization is to use the measured differences in arrival times of source signals at multiple pairs of receivers. In the literature, most algorithms that are based on time differences of arrival (TDOA) assume that the propagation velocity is a constant as a function of the source position, which is valid for air propagation or even for narrow band signals. However a bounded medium such as a concrete slab (encountered in indoor environment) is usually dispersive and damped. In this study, we demonstrate that under such conditions, the concrete slab can be assimilated to a thin plate; considering a Kelvin–Voigt damping model, we introduce the notion of perceived propagation velocity, which decreases when the source-sensor distance increases. This peculiar behavior precludes any possibility to rely on existing localization methods in indoor environment. Therefore, a new localization algorithm that is adapted to a damped and dispersive medium is proposed, using only on the sign of the measured TDOA (SO-TDOA). A simulation and some experimental results are included, to define the performance of this SO-TDOA algorithm.

Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems

Real-time user localization in indoor environments is an important issue in ambient assisted living (AAL). In this context, localization based on received signal strength (RSS) has received considerable interest in the recent literature, due to its low cost and energy consumption and to its availability on all wireless communication hardware. On the other hand, the RSS-based localization is characterized by a greater error with respect to other technologies. Restricting the problem to localization of AAL users in indoor environments, we demonstrate that forecasting with a little user movement advance (for example, when the user is about to leave a room) provides significant benefits to the accuracy of RSS-based localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS measurements and trained to recognize patterns of user’s movements to feed back to the RSS-based localization system.

Optimal receiver antenna location in indoor environment using dynamic differential evolution and genetic algorithm

Abstract Using the impulse responses of these multipath channels, the bit error rate (BER) performance for binary pulse amplitude modulation impulse radio ultra-wideband communication system is calculated. The optimization location of receiving antenna is investigated by dynamic differential evolution (DDE) and genetic algorithm (GA) to minimize the outage probability. Numerical results show that the performance for reducing BER and outage probability by DDE algorithm is better than that by GA.

Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns

In ubiquitous sensor networks (USN), sensor node localization is normally performed with global positioning system (GPS) or radio signal strength (RSS) between a target node and reference nodes. Because GPS is not available in indoor environments, RSS-based approach is commonly used for indoor localization. However, RSS-based approach is hard to be applied to real indoor environments (e.g., home) because of signal interferences with various indoor obstacles such as walls, doors, furniture, and electric appliances. In this paper, we propose an efficient indoor localization method for Zigbee sensor nodes by classifying link quality indicator (LQI) patterns between a target node and multiple reference nodes rather than using calculation with RSS values. And we also present the results of indoor localization experiments in our ubiquitous home network test bed using the proposed localization method.

A multiclassifier approach for topology-based WiFi indoor localization

People localization is required for many novel applications like for instance proactive care for the elders or people suffering degenerative dementia such as Alzheimer's disease. This paper introduces a new system for people localization in indoor environments. It is based on a topology-based WiFi signal strength fingerprint approach. Accordingly, it is a robust, cheap, ubiquitous and non-intrusive system which does require neither the installation of extra hardware nor prior knowledge about the structure of the environment under consideration. The well-known curse of dimensionality critically emerges when dealing with complex environments. The localization task turns into a high dimensional classification task. Therefore, the core of the proposed framework is a fuzzy rule-based multiclassification system, using standard methodologies for the component classifier generation such as bagging and random subspace, along with fuzzy logic to deal with the huge uncertainty that is characteristic of WiFi signals. Achieved results in two real environments are encouraging, since they clearly overcome those ones provided by the well-known nearest neighbor fingerprint matching algorithm, which is usually considered as a baseline for WiFi localization.

Self-organised localisation in indoor environments using the ALF framework

We present and discuss the autonomous localisation framework ALF, a self-organising indoor localisation environment. Location awareness is an important property for a growing number of applications. GPS is frequently used to provide this information in outdoor environments, but this is not applicable for indoor applications. There have been many approaches to solve the localisation problem for those GPS-denied scenarios. However, many of them are limited to certain hardware restrictions or do not provide robust self-localisation in dynamic real world application. ALF is a complete and modular framework based on minimal hardware requirements. The system is not only capable to deploying itself autonomously in unknown environments and offering position information among the participants, but it also supports accurate real-time localisation to customers. The concepts allows to remove or to add features e.g., the heading of nodes or certain real-time capabilities as the scenario demands or the even the used hardware changes. The awareness and handling of measurement errors, especially in non-line of sight NLOS cases, is an essential criterion for a real world application.

Mobile Robot Localization Using Fuzzy Segments

This paper presents the development of a framework based on fuzzy logic for multi-sensor fusion and localization in indoor environments. Such a framework makes use of fuzzy segments to represent uncertain location information from different sources of information. Fuzzy reasoning, based on similarity interpretation from fuzzy logic, is then used to fuse the sensory information represented as fuzzy segments. This approach makes it possible to fuse vague and imprecise information from different sensors at the feature level instead of fusing raw data directly from different sources of information. The resulting fuzzy segments are used to maintain a coherent representation of the environment around the robot. Such an uncertain representation is finally used to estimate the robot position. The proposed multi-sensor fusion localization approach has been validated with a mobile platform using different range sensors.

Comparison of Dynamic Differential Evolution and Genetic Algorithm for MIMO-WLAN Transmitter Antenna Location in Indoor Environment

A novel optimization procedure for the location of the transmitter in $$3\times 3$$ multiple input multiple output (MIMO) wireless local area network (WLAN) wireless communication systems is presen...

Fault detection and mitigation in WLAN RSS fingerprint-based positioning

Fingerprint-based positioning methods that utilise WLAN received signal strength (RSS) measurements are becoming increasingly popular for inferring terminal location in indoor environments. They are very attractive mainly because of the low deployment cost, as they exploit the ubiquity of WLAN access points (AP) and the worldwide availability of mobile devices featuring WLAN adapters for wireless connectivity. They provide reliable and accurate location estimates, however their performance can be compromised when faults occur in the overall positioning system. Such faults are injected in different ways, including the removal or relocation of one or several APs, and are caused either accidentally or deliberately due to malicious attacks. These faults corrupt the RSS fingerprints and may lead to accuracy degradation, unless countermeasures are taken. In this work, we focus on positioning under faults and construct detection schemes using fault indicators that are applicable to standard fingerprint-based methods and achieve high fault detection rates. In addition, we incorporate our detection mechanisms into hybrid positioning algorithms that greatly improve fault tolerance in the presence of different types of faults. Finally, we present experimental results to verify the effectiveness of the proposed approach.

Monocular Vision Based Navigation and Localisation in Indoor Environments

This paper describes a set of tools and algorithms to enable vision based navigation for a Micro Aerial Vehicle (MAV) flying in an indoor environment. An optical flow balancing strategy is used to avoid obstacles in the vehicle's path. An Extended Kalman Filter (EKF) based visual odometry system is described which can estimate the trajectory of the vehicle since low cost MEMS based Inertial Measurement Units (IMU) are not capable of providing drift free attitude and position information. To extract depth information using a single camera, a simple triangulation based method using a laser pointer is presented. This enables the MAV to detect oncoming obstacles directly in front of it and execute a turn to avoid collision in case the optical flow balancing algorithm is unable to detect an obstruction.

Development of Omni Directional Mobile Robot Navigation System using RFID for Multiple Object

Navigation is an important technique in the field of mobile robotics. A major field of mobile robot system is required to navigate in unknown environments accurately. By determining the positions and selecting a motion control of the robot, navigation is fulfilled through some technique. In this paper, a modular navigation technique in relation with signal from RFID tags, and RFID reader is developed. The main idea is to test the ability of mobile robot to navigate the location in indoor environments. The RFID reader is mounted on the mobile robot to communicate with the RFID tags to determine robot's position while the RFID tags is are placed at difference location. The position of mobile robot is determined based on the location of RFID tag. The actuator will be move according to the angle between the robot's current positioned and the target tag.

Indoor Localization System Based on an Ultrasonic Sensor Network

This paper proposes an ultrasonic localization system, which adopts ultrasonic sensors to set up a network and realizes the information interactively with control center via 485 Bus. Using TDOA algorithm realizes the localization in indoor environment, and proposes a method to gain node information, on the basis, we can get a higher degree of response speed and accuracy of the system. Take an experiment by constructing an indoor localization test terrace, to prove feasibility of the theoretical.

An Improved Technique for Robot Global Localization in Indoor Environments

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.

Optimal Relay Antenna Location in Indoor Environment Using Particle Swarm Optimizer and Genetic Algorithm

An optimization procedure for the location of the relay transceiver in ultra-wideband wireless communication system is presented. The impulse responses of different transceiver locations are computed by shooting and bouncing ray/image (SBR/Image) techniques and inverse fast Fourier transform (IFFT). By using the impulse responses of these multi-path channels, the bit error rate (BER) performance for binary pulse amplitude modulation (BPAM) impulse radio UWB communication system are calculated. Based on the BER performance, the outage probability for any given relay location of the transceiver can be computed. The optimal relay antenna location for minimizing the outage probability is searched by genetic algorithm (GA) and particle swarm optimizer (PSO). The transmitter is in the center of the whole indoor environment and the receivers are uniform distributed with 1.5 meter intervals in the whole indoor environment. Two cases are considered as following: (I) Two relay transceivers with two different cases are employed. First, the whole space is divided into two areas and one relay transceiver is used in each area. The optimal relay antenna locations are searched in each area respectively. Second, the two optimal relay locations are searched in the whole space directly without any prior division. (II) Four relay transceivers with two different cases are employed. First, the whole space is divided into four areas and one relay transceiver is used in each area. The optimal relay antenna locations are searched in each area respectively. Second, the four optimal relay locations are searched in the whole space directly without any prior division. Numerical results have shown that our proposed method is effective for finding the optimal location for relay antenna to reduce BER and outage probability.

Performance Evaluation of 3D-LOCUS Advanced Acoustic LPS

Local positioning systems (LPSs) based on acoustic transducers (mainly ultrasonic) offer accurate localization in indoor environments. However, their performance is commonly limited by the transducers' frequency bandwidth and emission pattern. 3D-LOCUS is a new advanced acoustic LPS that claims subcentimeter accuracy even in turbulent environments. This is achieved through the use of broadband omnidirectional transducers, suitable design of emitted signals, proper calibration, and bidirectional emissions. This paper evaluates the 3D-LOCUS LPS by providing results in terms of some positioning metrics such as accuracy, resolution, and coverage, which show how it outperforms other state-of-the-art LPS prototypes. 3D-LOCUS operating in bidirectional mode minimizes environmental effects, such as temperature and airflows, attaining localization errors below 9 mm with a 90% confidence level, an RMS error below 7.4 mm, and 5 mm resolution in a localization volume of 2 times 2 times 0.4 m3.

Evaluation of position tracking technologies for user localization in indoor construction environments

Evolving technologies such as location-aware computing offer significant potential of improving important decision-making tasks on construction sites by providing support for tedious and time-consuming tasks associated with timely and accurate access to project information. For example, rapid and convenient access to contextual project information, through continuous position tracking of engineers, managers, and inspectors, can lead to significant cost and time savings due to the accuracy and immediacy with which relevant project information can be made available to field personnel. Considering the spatial expanse and dynamic nature of typical construction projects, mobile users need to be constantly tracked both outdoors as well as indoors. The Global Positioning System (GPS) is an attractive option for outdoor environments, but is not suitable for indoor applications because it needs a clear line-of-sight to orbital satellites in order to track position. As a result, alternate means of tracking users' location in indoor environments without relying on GPS is needed. This paper presents research that investigated the effectiveness of three wireless technologies for dynamic indoor user position tracking. In particular, Wireless Local Area Networks (WLAN), Ultra-Wide Band (UWB), and Indoor GPS positioning systems are evaluated and compared. Experimental results demonstrate the ability of Indoor GPS, in particular, to estimate a mobile user's location with relatively low uncertainty (1 to 2 cm).

Gradient-based local affine invariant feature extraction for mobile robot localization in indoor environments

In this paper, we propose a gradient-based local affine invariant feature extraction algorithm (G-LAIFE), using affine moment invariants for robot localization in real indoor environments. The proposed algorithm is an effective feature extraction algorithm that is invariant to image translation and to 3D rotation, and it is within a partial range of the image scale. Representative performance analysis confirms that the proposed G-LAIFE algorithm significantly enhances the recognition rate and is more efficient than the scale invariant feature transform (SIFT), especially in terms of 3D rotation change and computational time.

Robot Localization in Indoor Environments Using Radio Frequency Identification Technology and Stereo Vision

In this paper, a novel method of obstacle recognition and localization for mobile robots using radio frequency identification (RFID) technology and stereo vision is proposed. It is inexpensive, flexible and easy to use in practical environments. As information about the obstacles or environment can be written in ID tags, the proposed method can detect the obstacles easily and quickly compared with other methods. RF is not so stable, so the Bayes rule was introduced to calculate the probability where the ID tag exists in order to improve the accuracy of localizing obstacles with the ID tags. Then the stereo camera starts to process the image of the region of interest where the obstacle exists. The proposed method does not need to process all the image and easily gets some information about obstacles such as size, and color, and thus decreases the processing computation. Research on RFID technology integrating stereo vision to localize an indoor mobile robot has also been performed. This paper introduces the architecture of the proposed method and some experimental results.

A New Sonar-based Landmark for Localization in Indoor Environments

This paper presents a new sonar based landmark to represent significant places in an environment for localization purposes. This landmark is based on extracting the contour free of obstacles around the robot from a local evidence grid. This contour is represented by its curvature, calculated by a noise-resistant function which adapts to the natural scale of the contour at each point. Then, curvature is reduced to a short feature vector by using Principal Component Analysis. The landmark calculation method has been successfully tested in a medium scale real environment using a Pioneer robot with Polaroid sonar sensors.

3D ARTIFICIAL LANDMARK DESIGN AND RECOGNITION FOR MOBILE ROBOTS

The robot localization is an important problem in mobile robotics. This work addresses the problem of global localization in indoor environment by using vision-based technique. This paper proposes innovative idea of using the 3D artificial landmark and the 3D Photonic Mixer Device (PMD) camera for robot localization. Since the 3D camera provides a ready-to-use depth image, there is no extra effort needed to enable the computation of 3D image reconstruction as in stereo vision system and also the range image from PMD camera can be obtained under very low visibility condition. The design of the 3D landmark and the recognition technique can be applicable for other range sensors.