Robust Localization Research Articles

Development of an Omnidirectional AGV by Applying ORB-SLAM for Navigation Under ROS Framework

The development of an automated guided vehicle with omni-wheels for autonomous navigation under a robot operating system framework is presented in this paper. Specifically, a laser rangefinder-constructed two-dimensional environment map is integrated with a three-dimensional point cloud map to achieve real-time robot positioning using the oriented features from accelerated segment test and rotated binary robust independent elementary feature detector-simultaneous localisation and mapping algorithm. In the path planning for autonomous navigation of the omnidirectional mobile robot, we applied the A* global path search algorithm, which uses a heuristic function to estimate the robot position difference and searches for the best direction. Moreover, we employed the time-elastic-band method for local path planning, which merges the time interval of two locations to realise time optimisation for dynamic obstacle avoidance. The experimental results verified the effectiveness of the applied algorithms for the omni-wheeled mobile robot. Further, the results showed a superior performance over the adaptive Monte Carlo localisation for robot localisation and dynamic window approach for local path planning.

An experimental comparison of Deep Learning strategies for AUV navigation in DVL-denied environments

Accurate and robust navigation and localisation systems are critical for Autonomous Underwater Vehicles (AUVs) in order to perform missions in challenging environments. However, since the Global Positioning System (GPS) is not available in the underwater domain, the localisation task is commonly fulfilled by integrating direct linear speed readings provided by a Doppler Velocity Log (DVL) over time. As a consequence, DVL failures or fallacies and DVL-denied environments may arise as unexpected causes for severe malfunctions of the whole navigation system. Motivated by these considerations and the outstanding performance of Deep Neural Networks (DNNs) in supervised regression problems, a Deep Learning (DL) -based approach has been developed to estimate the vehicle’s body-frame velocity, without canonically employing DVL measurements, in a Dead-Reckoning (DR) navigation strategy. In particular, this work will describe the whole framework, starting from the data gathered by the AUVs of the National Oceanography Centre (NOC) during different field campaigns, through to the data pre-processing and the inference of the predicted velocity. Finally, a comprehensive offline comparison between different DL-based models is presented to assess the validity of the proposed approach.

Robust train localisation method based on advanced map matching measurement-augmented tightly-coupled GNSS/INS with error-state UKF

AbstractThis paper presents a robust train localisation system by fusing a Global Navigation Satellite System (GNSS) with an Inertial Navigation System (INS) in a tightly-coupled (TC) strategy. To improve navigation performance in GNSS partly blocked areas, an advanced map-matching (MM) measurement-augmented TC GNSS/INS method is proposed via an error-state unscented Kalman filter (UKF). The advanced MM generates a matched position using a one-step predicted position from a UKF time update step with binary search algorithm and a point–line projection algorithm. The matched position inputs as an additional measurement to fuse with the INS position to augment the degraded GNSS pseudorange measurement to optimise the state estimation in the UKF measurement update step. Both the real train test on the Qinghai–Tibet railway and the simulation were carried out and the results confirm that the proposed advanced MM measurement-augmented TC GNSS/INS with error-state UKF provides the best horizontal positioning accuracy of 0 ⋅ 67 m, which performs an improvement of about 71% and 90% with respect to TC GNSS/INS with only error-state UKF and only error-state Extended Kalman filter in GNSS partly blocked areas.

Robust Simultaneous Localization and Mapping Using Range and Bearing Estimation of Radio Ultra High Frequency Identification Tags

In this article, we consider an indoor simultaneous localization and mapping (SLAM) problem for a mobile robot measuring the phase of the signal backscattered by a set of passive radio ultra high frequency identification (ID) tags, deployed in unknown position on the ceiling of the environment. The solution approach is based on the introduction, for each radio frequency identification (RFID) tag observed, of a multihypothesis extended Kalman filter (MHEKF) which, based on the measured phases and on the wheel encoder readings, provides an estimate of the range and of the bearing of the observed tag with respect to the robot. This information is then used in an extended Kalman filter (EKF) solving the SLAM problem. Since an effective range and bearing estimate is available only after some steps, a resilient module is added to the algorithm to evaluate the reliability of the position estimate of each observed tag. As shown through numerical and experimental results, this makes the proposed approach robust with respect to several kinds of unmodeled disturbances, like multipath effects or even the unexpected change of the position of a tag.

Outside Front Cover: Volume 2 Issue 2

Interdisciplinary MaterialsVolume 2, Issue 2 p. i-i COVER IMAGEOpen Access Outside Front Cover: Volume 2 Issue 2 First published: 20 March 2023 https://doi.org/10.1002/idm2.12081AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Outside Front Cover Topological objects such as Möbius strip and Klein bottle display fantastic shapes in geometric space. Artificial acoustic metamaterials can make their band structures topological in momentum space, exhibiting novel and robust wave propagation or localization in lower dimensions. Recent studies of various topological phases associated with abnormal effects in acoustics further advance topological physics. Volume2, Issue2March 2023Pages i-i RelatedInformation

Robust Anomaly Clue Localization of Multi-Dimensional Derived Measure for Online Video Services

Anomaly clue localization of multi-dimensional derived measure is vitally important for the reliability of online video services. In this paper, we propose RobustSpot, an end-to-end framework for localizing the clues to anomalous multi-dimensional derived measures. RobustSpot integrates two novel indicators, i.e., “Anomaly Degree” and “Contribution Ability”, with a simple yet effective method, weighted association rule mining (WARM), to automatically mine the hidden relationships across data dimensions for localizing the most likely clues to the root cause. Using 135 real-world cases collected from a top-tier global online video service provider <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> with 170+ million monthly active users, we demonstrate that RobustSpot achieves high accuracy (Top-5 accuracy of 98%), significantly outperforming state-of-the-art methods. The average localization time of RobustSpot is 1.83s, which is satisfying in our scenario. We have open-sourced the implementation of RobustSpot as well as the data used in the evaluation experiments.

Long-Term Visual Simultaneous Localization and Mapping: Using a Bayesian Persistence Filter-Based Global Map Prediction

With the rapidly growing demand for accurate localization in real-world environments, visual simultaneous localization and mapping (SLAM) has received significant attention in recent years. However, those existing methods still suffer from the degradation of localization accuracy in long-term changing environments. To address these problems, we propose a novel long-term SLAM system with map prediction and dynamics removal. First, a visual point-cloud matching algorithm is designed to efficiently fuse 2D pixel information and 3D voxel information. Second, each map point is classified into three types: static, semistatic, and dynamic based on the Bayesian persistence filter (BPF). Then we remove the dynamic map points to eliminate the influence of those map points. We can obtain a global predicted map by modeling the time series of semistatic map points. Finally, we incorporate the predicted global map into a state-of-the-art SLAM method, achieving an efficient visual SLAM system for long-term, dynamic environments. Extensive experiments are carried out on a wheelchair robot in an indoor environment over several months. The results demonstrate that our method has better map prediction accuracy and achieves more robust localization performance.

Perception-Aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This work proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using four-dimensional (4D) building information models (BIM), considering the project progress, safety requirements, and UAV’s localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.

An accurate approach of device-free localization with attention empowered residual network

Device-free localization (DFL) has been recognized as an emerging technology in the Internet of Things (IoT) field. Based on soft computing techniques, e.g., cloud systems, neural networks, wireless sensor networks, etc., DFL can locate the targets that do not carry any equipment. A crucial problem for the DFL is how to extract significant patterns from weak signal variations in order to achieve high accuracy and robustness. To solve this issue, we transform the DFL procedure into a classification task. In this paper, a channel-dependent attention empowered residual network (CA-ResNet) with three residual layers is designed for precise localization by extracting underlying signal features. The CA-ResNet takes the advantages of the residual connection and the attention mechanism in feature extraction, which is expected to have a high level of robustness in the face of extreme noise and abnormal circumstances for DFL. Experiment results based on real-world data demonstrate that our proposed approach provides a high level of localization performance for a suitable grid size. For instance, the CA-ResNet approach achieves high localization accuracy of almost 100% in general cases. Under the condition of introducing noise to data, e.g., a signal to noise ratio greater than −5 dB, our proposed method maintains high accuracy and strong robustness. Compared with the state-of-the-art DFL methods in several cases, the best localization accuracy obtained by our proposed method is 16.4% better than the compared AugRF, 15.5% better than the radio robust image fingerprint indoor localization algorithm (RRIFLoc), and 6.9% better than the convolutional autoencoder neural network, especially in challenging cases that partial sensor nodes are broken or power off under noisy condition.

A Multi-Layered 3D NDT Scan-Matching Method for Robust Localization in Logistics Warehouse Environments

This paper proposed a multi-layered 3D NDT (normal distribution transform) scan-matching approach for robust localization even in the highly dynamic environment of warehouse logistics. Our approach partitioned a given 3D point-cloud map and the scan measurements into several layers regarding the degree of environmental changes in the height direction and computed the covariance estimates for each layer using 3D NDT scan-matching. Because the covariance determinant is the estimate's uncertainty, we can determine which layers are better to use in the localization in the warehouse. If the layer gets close to the warehouse's floor, the degree of environmental changes, such as the cluttered warehouse layout and position of boxes, would be significantly large, while it has many good features for scan-matching. If the observation at a specific layer is not explained well enough, then the layer for localization can be switched to other layers with lower uncertainties. Thus, the main novelty of this approach is that localization robustness can be improved even in very cluttered and dynamic environments. This study also provides the simulation-based validation using Nvidia's Omniverse Isaac sim and detailed mathematical descriptions for the proposed method. Moreover, the evaluated results of this study can be a good starting point for further mitigating the effects of occlusion in warehouse navigation of mobile robots.

Enhanced piezoelectric energy harvesting based on sandwiched phononic crystal with embedded spheres

In recent years, metamaterial/ phononic crystal (PnC) based energy harvesters are gaining interest due to their excellent elastic wave manipulation and energy trapping capabilities. Here, we propose a novel PnC comprising of Tungsten Carbide (WC) spheres embedded in epoxy resin matrix. The sphere-epoxy composite is encapsulated by Aluminum (Al) holey structure and the device is sandwiched between two Al plates. Numerical analysis of band structure reveals a wide phononic band gap (BG) from 50.65 kHz to 71.12 kHz. These BGs can be engineered by varying geometric parameters of the unit cell viz., the radius of the sphere and thickness of Al plates. A point defect is introduced by removing the central sphere of the 5 × 5 PnC to facilitate the robust localization of evanescent wave defect modes within the bandgap. Moreover, it is observed that, by altering the radius of the defect sphere, the number of defect modes and their shift can be reconfigured. A PnC based energy harvester is implemented by attaching a piezoelectric disk (PZT-5H) onto the defect PnC just above the defect site. This arrangement of PZT disk converts the highly resonant mechanical defect mode into electrical energy, thereby allowing vibration energy harvesting. Finally, we show that the power enhancement can be achieved by ∼12 times with the proposed PnC compared to the bare Al block.

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system.

LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5-10 cm mapping accuracy and 20-30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.

Robust Localization for Near- and Far-Field Signals with an Unknown Number of Sources

Source location is a constant issue of importance of both theoretical study and practical engineering. Many pioneers have put out the corresponding solutions for near- or far-field signals, and preferred contributions are suggested. To our best knowledge, there are currently few focused approaches to the complicated situation where both near- and far-field signals exist with an unknown number of sources. Additionally, the robustness of the method must be taken into account when the additive background noise does not follow Gaussian or super-Gaussian distribution. To solve these problems, a novel method based on phased fractional lower-order moment (PFLOM) is proposed to simultaneously better preserve the signal and suppress the noise. Secondly, the whole procedure of the method containing direction of arrival (DOA) estimation, range estimation, separation of near-and far-field sources, and crucial parameter settings are studied in detail. Finally, comprehensive Monte Carlo experiments are carried out in the simulation to demonstrate the superiority of the proposed method compared to the existing competitive methods. Due to the novel method’s effectiveness with an unknown number of sources and robustness against various noises, it is believed that it could be fully utilized in more fields.

Radar Target Localization with Multipath Exploitation in Dense Clutter Environments

The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.

Efficient iteratively reweighted algorithms for robust hyperbolic localization

Hyperbolic localization utilizes the time-difference-of-arrival (TDOA) measurements obtained from multiple coordinated sensors to locate an emitting source. A challenge in such an area, is that adverse factors like the non-line-of-sight propagation in typical environments may lead to outlying sensor observations and therefore deteriorated positioning performance. To mitigate the negative effects of outliers in the raw TDOA-based range-difference (RD) data on localization accuracy, the conventional ℓ2-norm based RD linear least squares (RD-LLS) location estimator is robustified here using the Geman–McClure (GM) and correntropy-induced loss functions, respectively. Two soft-redescending M-estimators are consequently developed, whereafter two iterative algorithms are presented to handle the corresponding optimization formulations. The first method applies the iteratively reweighted least squares to implement GM loss minimization, whereas the second employs the technique of half-quadratic optimization, through which the correntropy loss is maximized in an alternating manner. Either way the problem comes down to updating a series of weight-like variables and tackling a simplifed formulation that is closely related to RD-LLS at each iteration, thereby leading to fairly computationally efficient iterative solutions. Numerical results confirm the capabilities of the proposed schemes to outperform several existing hyperbolic positioning approaches.

RRIFLoc: Radio Robust Image Fingerprint Indoor Localization Algorithm Based on Deep Residual Networks

Indoor localization is one of the most exciting research areas due to the increasing demand for location-based services (LBSs) in indoor environments. The fingerprint positioning method of the received signal strength indicator (RSSI) is widely used in indoor localization due to its simple deployment and low cost. However, since the RSSI is affected by indoor environment changes and the heterogeneous nature of devices, it is easy to cause fingerprint drift and disappearance of fingerprint features, resulting in low accuracy and weak robustness of indoor localization. In this article, we propose a robust indoor localization method that is calibrated-free of Wi-Fi image fingerprints, called the radio robust image fingerprint localization (RRIFLoc) algorithm. First, the signal strength difference (SSD) fingerprint and RSSI kurtosis are derived from the RSSI fingerprint. SSD and kurtosis alleviate the low positioning accuracy and weak anti-interference caused by fingerprint drift and the disappearance of fingerprint features. Second, the fusion of the RSSI, SSD, and kurtosis is constructed into a radio robust image fingerprint (RRIF). Finally, we build the RRIFLoc model using the generated RRIF and the deep residual network for location estimation. According to experiments on a public dataset, our method reduces the average location estimation error by 56.87% compared to state-of-the-art indoor fingerprint localization methods.

A robust indoor localization algorithm based on polynomial fitting and Gaussian mixed model

Wireless sensor network (WSN) positioning has a good effect on indoor positioning, so it has received extensive attention in the field of positioning. Non-line-of sight (NLOS) is a primary challenge in indoor complex environment. In this paper, a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error. Firstly, fitting polynomials are used to predict the measured values. The residuals of predicted and measured values are clustered by Gaussian mixture model (GMM). The LOS probability and NLOS probability are calculated according to the clustering centers. The measured values are filtered by Kalman filter (KF), variable parameter unscented Kalman filter (VPUKF) and variable parameter particle filter (VPPF) in turn. The distance value processed by KF and VPUKF and the distance value processed by KF, VPUKF and VPPF are combined according to probability. Finally, the maximum likelihood method is used to calculate the position coordinate estimation. Through simulation comparison, the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper. And it shows strong robustness in strong NLOS environment.

Snapshot-deficient active target localization in beam-time domain using multi-frequency expectation-maximization algorithm.

The two-dimensional (2D) active target localization is generally hindered by the high temporal and spatial sidelobe levels in snapshot-deficient scenarios, where the adaptive approaches undergo performance degeneration since they require many snapshots to build the sample covariance matrix. Aiming at working robustly in snapshot-deficient active scenarios, a 2D expectation-maximization-based vertical-time-record (EMVTR) approach is proposed to compensate for the snapshot deficiency and achieve the high-resolution active localization by reconstructing the covariance matrix using estimated hyperparameters, i.e., signal powers and noise variance. With the short-time Fourier transform, the proposed approach could reduce echoes' temporal correlation and attain robust beam-time localization in mild reverberation. The multi-frequency EMVTR is derived from the single-frequency case to improve the weak echo localization. The performance is evaluated by considering single and multiple target echoes in simulation and a single moving target with tank experimental data. The results manifest the proposed EMVTR's robustness and effectiveness for the 2D active localization in snapshot-deficient scenarios.

Integrating spatial biophysical simulations across scales and methods.

The Allen Institute for Cell Science aims to understand the principles by which human induced pluripotent stem cells (hiPSCs) establish and maintain robust dynamic localization of cellular structures, and how they transition between states during differentiation and disease. The Simularium project aims to involve the wider biology community, especially wet lab biologists, in building and analyzing spatial mechanistic simulations at different levels of scale, and connecting them in the context of whole cells. We have composed multiple types of models that simulate objects in 3D space at multiple time and length scales and with different representations, in order to compare their behavior and outputs. To connect these models we have built interfaces using the Vivarium simulation framework, which allows for the flexible composition of many different models, and we are aiming to build multiscale models with adaptive resolution and calculation methods. To demonstrate this approach for actin dynamics, we have built interfaces between the community software engines ReaDDy, Cytosim, and MEDYAN, so that the composed model can be configured to simulate actin fibers either with coarse-grained monomers or filament lines. We have defined simple models that can translate between each of these simulation engines as unit tests for physical properties that are required for more complex models of endocytosis and leading edge dynamics of crawling cells. These test models explore how actin properties like bend and twist coupling, persistence length, and force generation differ when simulated with different methods and in different parameter regimes. We visualize the outputs interactively in 3D using the web-based Simularium viewer to explore differences between models and the implications for reproducible modeling of actin filaments across scales and methods.