Reduction Of Localization Error Research Articles

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Abstract Traditional vision-based inertial odometry (VIO) suffers from significant visual degradation, which substantially impacts state estimation in challenging lighting environments. Thermal imaging cameras capture images based on the thermal radiation of objects, rendering them impervious to lighting variations. However, integrating thermal infrared data into conventional visual odometry poses challenges due to its low texture, poor contrast, and high noise levels. In this paper, we propose a tightly coupled approach that seamlessly integrates information from visible light cameras, thermal imaging cameras, and inertial measurement units (IMUs). First, we employ adaptive bilateral filtering and Sobel gradient enhancement to smooth infrared images, thereby reducing noise and enhancing edge contrast. Second, we leverage the Sage-Husa adaptive filter in conjunction with iterative Kalman filtering (IEKF) to effectively mitigate the impact of non-Gaussian noise on the system. Finally, we conduct comprehensive evaluations of the proposed system using both open datasets and real-world experiments across four distinct scenarios: normal lighting, low-light conditions, low-light conditions with camera shake, and challenging lighting environments. Comparative analysis reveals that our method outperforms IEKF, yielding a reduction in localization error measured by root mean square error (RMSE) by 58.69%, 57.24%, 60.23%, and 30.87% in these respective scenarios.

Proposal of a wireless sensor network for footstep localization and optimization of its location using bio-inspired metaheuristics

This paper introduces a proposal for a wireless network of smart sensors designed to capture the vibrations produced by the impact of a person’s footsteps on a dispersive and damped floor. These vibrations are utilized to pinpoint the location of impacts using a heuristic algorithm, enabling operation at a low data transmission/reception rate. Traditionally, the placement of sensors within a room has not been given significant consideration in the context of localization; however, our findings indicate that a detailed analysis of both the number and location of sensors can substantially enhance the accuracy of footstep localization. In this study, we have optimized the arrangement and quantity of sensors through the application of bio-inspired metaheuristics, aiming to minimize localization errors across the room. Upon evaluating various bio-inspired metaheuristic optimization algorithms, we identified the one that yielded the lowest estimation errors for the room as a whole. Our experimental tests demonstrate that such optimization significantly enhances the efficacy of the localization algorithm, resulting in a reduction of localization error ranging from 18.24% to 46.78% across different trajectories.

Multi – Robot Exploration Supported by Enhanced Localization with Reduction of Localization Error Using Particle Swarm Optimization

Exploration of an area by a group of robots is an active research field of robotics as multi-robot exploration is applied extensively in several real life scenarios. The major challenges in such exploration are the availability of communication infrastructure as communication plays a key role in the coordination of team of robots for effective coverage of the area under exploration. But in disaster affected scenarios, there will be no existing communication infrastructure available and this makes the exploration ineffective and time consuming. Another challenge is in the localization process each robot is carrying out to update the map as well as for exchange of information with other robots. In this paper, an enhanced Multi-robot exploration strategy is introduced. The base of the exploration strategy is two techniques. The first one being localization of each robot involved in the exploration and this is done with the help of trilateration where three anchors are required which will be setup before the exploration starts. The second part is navigation and avoiding overlapping or missing out sectors while exploring. This is done with help of a navigation policy called frontier cell based approach. Further to this, the exploration strategy is supported with localization error reduction scheme in which the localization error is reduced with the help of Particle Swarm Optimization (PSO). The entire scheme is simulated and exploration time is analyzed for the same environment in different obstacle density and different number of robots to perform exploration. The results show the scheme is better than many existing multi-robot exploration strategies. Precisely, the proposed scheme is able to reduce the localization error to a threshold level of 0.02cm or below which can be considered as novel contribution towards the exploration strategies.

Unmanned aerial vehicle localization for device-to-device communication in fifth generation networks using modified penguin search optimization

The localization of multiple Unmanned Aerial Vehicle (UAV) in Device to Device (D2D) communication has been investigated for Internet of Things (IoT) based Fifth Generation (5 G) network. The localization in heterogeneous UAV-D2D network is formulated using bioinspired Modified Penguin Search Optimization (MPGSO) algorithm. In MPGSO, the reduction in localization error is carried by tracking the position of UAV. The intra-UAV and inter-UAV communication search strategy is realized in accordance to inter-group and intra-group communication. The proposed localization by means of UAV exploration allows the fast convergence towards the local optimum. In addition to intensification in MPGSO, the diversification of nodes leads to the exploitation of UAV nodes that have travelled from the actual position. This avoids the convergence towards the local optima. The localization with MPGSO provide an improvement of 3.7%, 5.75% and 7.8% as compared to conventional artificial bee colony, particle swarm optimization and genetic algorithm, respectively.

An Experimental Evaluation of Radio Models for Localizing Fixed-Wing UAVs in Rural Environments

The emergence of unmanned aerial vehicles (UAVs) has spurred research on radio air-to-ground (AG) channels. The research shows the AG channel can exhibit higher rates of change and more 3D effects than a terrestrial channel. A received-power model predicts the power that makes it from the transmitter (Tx) to the receiver (Rx) through the intervening environment. Received-power models are key to many RF localization schemes. However, many of the received-power models used for UAV localization are 1D or 2D; 3D effects such as ground reflection and polarization are oversimplified or ignored entirely. In this paper, we perform a comparative experimental analysis of various received-power models. The more sophisticated models account for range, Tx directionality, Rx directionality, polarization, and ground reflection. The models are applied to an AG link realized by a fixed-wing UAV in an open-field environment. Our results show that, on average, a 50% reduction in localization error can be achieved by including the polarization factor in the received-power model.

Device Free Indoor Localization in the 28 GHz band based on machine learning

By exploiting the received power change in a communication link produced by the presence of a human body in an otherwise empty room, this work evaluates indoor free device localization methods in the 28 GHz band using machine learning techniques. For this objective, a database is built using results from ray tracing simulations of a system comprised of 4 receivers and up to 2 transmitters, while a person is standing within the room. Transmitters are equipped with uniform linear arrays that switch their main beams sequentially at 21 angles, whereas the receivers operate with omnidirectional antennas. Statistical localization error reduction of at least 16% over a global-based classification technique can be obtained through the combination of two independent classifiers using one transmitter and a reduction of at least 19% for 2 transmitters. An additional improvement is achieved by combining each independent classifier with a regression algorithm. Results also suggest that the number of examples per class and size of the blocks (strips) in which the study area is partitioned play a role in the localization error.

Learning-Based Bias Correction for Time Difference of Arrival Ultra-Wideband Localization of Resource-Constrained Mobile Robots

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices-making it especially suited for resource-constrained multi-robot applications. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. In this letter, we address these issues by proposing a robust UWB TDOA localization framework comprising of (i) learning-based bias correction and (ii) M-estimation-based robust filtering to handle outliers. The key properties of our approach are that (i) the learned biases generalize to different UWB anchor setups and (ii) the approach is computationally efficient enough to run on resource-constrained hardware. We demonstrate our approach on a Crazyflie nano-quadcopter. Experimental results show that the proposed localization framework, relying only on the onboard IMU and UWB, provides an average of 42.08% localization error reduction (in three different anchor setups) compared to the baseline approach without bias compensation. We also show autonomous trajectory tracking on a quadcopter using our UWB TDOA localization approach.

Spectrally resolved passive acoustic imaging

Ultrasound in combination with circulating microbubble contrast agents has emerged as a promising modality for therapy and imaging of brain diseases. Despite progress, the need for spatio-temporal mapping and control of the cerebrovascular microbubble dynamics necessitates the development of fast and spectrally resolved trans-skull imaging methods. In this talk, we will present our recent efforts towards establishing such methods. First, we will introduce a general methodology using the angular spectrum (AS) approach in a heterogeneous medium, which is based on a numerical marching scheme to approximate the full implicit solution, and reveal its functionality for trans-skull passive acoustic imaging (70% source localization error reduction from 2.89 ± 1.76 mm to 0.68 ± 52 mm). Then, we will demonstrate the ability of AS in combination with a nonlinear state controller that uses specific frequency bands of the microbubble acoustic emissions (harmonic, ultra-harmonic, etc.) to attain stable cavitation activity for tens of seconds. Finally, we will present our progress towards adapting the AS method for rapid visualization of vessel structures with diameters of a few hundreds of microns. The superior image quality and computation efficiency (>100-fold faster from time-domain delay-and-sum methods) of AS offers unique opportunities for spectrally resolved passive acoustic imaging and image guided therapy of human disease.

Indoor Localization System Using Dual-Frequency Bands and Interpolation Algorithm

As an important part of the Internet of Things, WiFi-based fingerprint indoor localization technology has been extensively studied due to the widespread deployment of WLAN in indoor environments. Nevertheless, there are two stubborn issues in indoor localization using the fingerprint method, one is the additional localization error caused by heterogeneous devices and the other is the huge time consumed by upgrading the fingerprint database. Here, we propose a dual-frequency difference and cubic spline interpolation (DFD-CSI) localization system in this article. First, according to the characteristics of commercial WiFi router generally supports 2.4- and 5-GHz dual bands, a fingerprint database is established using these two bands’ data and their difference data. The existence of difference solves the problem of device heterogeneity, while the existence of dual-frequency data makes the positioning result more robust. In addition, we also use cubic spline interpolation to upgrade the rough fingerprint database in order to reduce the time of site exploration on the premise of ensuring localization accuracy. Extensive experiments have been conducted in a campus hall. Results show that DFD-CSI is robust against altered devices and access points (APs) compared with the traditional heterogeneous devices algorithms and crowdsourcing update methods, achieving a mean error of 3.7 m and 27.5% localization error reduction with APs changed.

Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks

Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.

Design and analysis of distance error correction–based localization algorithm for wireless sensor networks

AbstractLocalization error is the main criteria to decide the efficiency on any localization approach. In case of range‐free hop‐based localization approaches, position coordinates estimation is done through the common steps like the calculation of the minimum hop count, hop size, and then later on applying the multilateration or least square approach. There are many improvements that have been made in the form of various algorithms through some changes in the common steps for the reduction of localization error. It has been observed that the existing algorithms are still having the highest value of localization error. To overcome the deficiencies in the existing algorithms, a novel distance error correction metric–based approach has been introduced. This paper presents a distance error correction–based algorithm (DEC‐hop) for the hop‐based localization approach for a wireless sensor network. Distance‐based error correction factor that is introduced in this paper further reduces the localization error value to some extent. For measuring the effectiveness and the preciseness of the given approach, it is inspected with other improved algorithms based on DV‐hop. Simulation results have confirmed that the proposed error correction–based algorithm outperforms the other variety of improved hop‐based algorithms. Results are analyzed in terms of various factors like the localization error, accuracy, and error variance against the parameters of interest taken as node amount, anchor ratio, and communication range of the node.

Study of range free centroid based localization algorithm and its improvement using particle swarm optimization for wireless sensor networks under log normal shadowing

Localization deals with the determination of coordinates of unknown nodes for proper routing of data in wireless sensor networks. The centroid based localization algorithm (CLA) has been explored to a great extent till date. Its basic and improved form suffers from large localization error. In the present work the basic range free centroid based localization algorithm is studied under log normal shadowing and evaluated in terms of localization error. Further the basic CLA is improved by using the particle swarm optimization (PSO) under the same environment. The localization error for basic and PSO based CLA is calculated by varying the anchor ratio, communication range, number of unknown nodes, and network area. In every condition the PSO based CLA performs well over basic CLA in terms of reduction in localization error. The localization error in the proposed work is smallest as compared to other known centroid based localization algorithms.

Quantitative estimation of localization errors of 3d transition metal pseudopotentials in diffusion Monte Carlo.

The necessarily approximate evaluation of non-local pseudopotentials in diffusion Monte Carlo (DMC) introduces localization errors. We estimate these errors for two families of non-local pseudopotentials for the first-row transition metal atoms Sc-Zn using an extrapolation scheme and multideterminant wavefunctions. Sensitivities of the error in the DMC energies to the Jastrow factor are used to estimate the quality of two sets of pseudopotentials with respect to locality error reduction. The locality approximation and T-moves scheme are also compared for accuracy of total energies. After estimating the removal of the locality and T-moves errors, we present the range of fixed-node energies between a single determinant description and a full valence multideterminant complete active space expansion. The results for these pseudopotentials agree with previous findings that the locality approximation is less sensitive to changes in the Jastrow than T-moves yielding more accurate total energies, however not necessarily more accurate energy differences. For both the locality approximation and T-moves, we find decreasing Jastrow sensitivity moving left to right across the series Sc-Zn. The recently generated pseudopotentials of Krogel et al. [Phys. Rev. B 93, 075143 (2016)] reduce the magnitude of the locality error compared with the pseudopotentials of Burkatzki et al. [J. Chem. Phys. 129, 164115 (2008)] by an average estimated 40% using the locality approximation. The estimated locality error is equivalent for both sets of pseudopotentials when T-moves is used. For the Sc-Zn atomic series with these pseudopotentials, and using up to three-body Jastrow factors, our results suggest that the fixed-node error is dominant over the locality error when a single determinant is used.

Indoor Localization and Automatic Fingerprint Update with Altered AP Signals

Wi-Fi fingerprinting has been extensively studied for indoor localization due to its deployability under pervasive indoor WLAN. As the signals from access points (APs) may change due to, for example, AP movement or power adjustment, the traditional approach is to conduct site survey regularly in order to maintain localization accuracy, which is costly and time-consuming. Here, we study how to accurately locate a target and automatically update fingerprints in the presence of altered AP signals (or simply, “altered APs”). We propose L ocalization with A ltered A Ps and F ingerprint U pdating (LAAFU) system, employing implicit crowdsourced signals for fingerprint update and survey reduction. Using novel subset sampling, LAAFU identifies any altered APs and filter them out before a location decision is made, hence maintaining localization accuracy under altered AP signals. With client locations anywhere in the region, fingerprint signals can be adaptively and transparently updated using non-parametric Gaussian process regression. We have conducted extensive experiments in our campus hall, an international airport, and a premium shopping mall. Compared with traditional weighted nearest neighbors and probabilistic algorithms, results show that LAAFU is robust against altered APs, achieving 20 percent localization error reduction with the fingerprints adaptive to environmental signal changes.

A New Iris Detection Method based on Cascaded Neural Network

Iris recognition is one of the most reliable and applicable methods for a person's identification. The most complex and important phase of recognition is iris segmentation of an input eye image that affects iris recognition successful rate significantly. Due to missed parameters in noisy images, main error occurs in the performance of classic localization. Artificial neural networks (ANN) are appropriate substitutes for classic methods because of their flexibility on noisy images. In this paper, we use feedforward neural network (FFNN) for the improvement of iris localization accuracy. We apply two methods in order to reduce neural network error: first, designing one neural network for each output neuron .Second, using cascaded feedforward neural network (CFFNN). Then, we examine proposed methods on different datasets which cause remarkable reduction of localization error.

L-VIRT: Range-free 3-D localization of RFID tags based on topological constraints

RFID is an automatic identification technique that stores and remotely retrieves data on small devices called tags. This technology enables tracking of people and objects, supporting the conception of pervasive networks if identities are linked in real-time to their locations. We propose L-VIRT, a 3-D localization method for RFID tags based only on connectivity information. Virtual landmarks are combined with defined topological constraints to localize tags. A relaxation of the constraints is introduced to attenuate the effect of reading errors and, thus, to increase the precision of the localization. Simulation results show a fine-grained accuracy in free space or moderately obstructive environments, and a satisfactory accuracy in very cluttered environments. Moreover, the use of additional mobile readers enables rapid and large localization error reduction in sparse deployments of static readers.