Robust Localization Algorithm Research Articles

Attitude solving aided UWB/IMU integrated algorithm with real-time NLOS suppression

In response to the challenges posed by non-line-of-sight (NLOS) errors and inadequate attitude estimation accuracy in ultra-wideband and inertial measurement unit (UWB/IMU) integrated navigation algorithms in the complex environment, a robust UWB/IMU integrated positioning scheme is proposed. On one hand, the utilization of the robust local weighted regression algorithm (RLWR) is employed to mitigate the impact of NLOS errors on UWB data. RLWR incorporates information from nodes with known pseudo-range within local time intervals into the regression model, enhancing the identification of NLOS errors and improving positioning accuracy. On the other hand, the variational Bayesian filter algorithm based on adaptive conjugate gradient descent (ACGD) is proposed to improve the accuracy of IMU attitude calculation. The algorithm leverages an ACGD approach to optimize the attitude output of the accelerometer and magnetometer. The output is then incorporated into the variational Bayesian filtering system alongside the gyroscopic attitude output compensated by integrated positioning. Compared to conventional quaternion calculation and gradient descent linear filtering methods, the approach exhibits superior precision and stability. The experimental findings demonstrate that the amalgamation of the proposed NLSO identification suppression algorithm and the enhanced attitude computation algorithm confers significant advantages in terms of both localization accuracy and attitude estimation precision in complex environments. Moreover, the robust solution presented in the paper ensures the preservation of filter performance in the event of UWB measurement failure.

Robust Kalman filter for fractional order systems with uncertain colored noise variance

For fractional order systems with colored process noise, the discretization fractional order system model is used to construct the augmented vector defined by the state vector and colored process noise vector. Based on the augmented equation of fractional order systems, the robust local Kalman filtering algorithm for fractional order systems with colored process noise is derived. The matrix weighted fusion, weighted measurement fusion and centralized fusion methods were used to fuse and estimate the state of multi-sensor fractional order system. Simulation results show the effectiveness of the proposed algorithm.

A Graph-Based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.

High Precision and Robust Vehicle Localization Algorithm with Visual-LiDAR-IMU Fusion

High Precision and Robust Vehicle Localization Algorithm with Visual-LiDAR-IMU Fusion

Robust multi-model mobile target localization scheme based on underwater acoustic sensor networks

Underwater mobile target localization based on underwater acoustic sensor networks (UASNs) is a critical application in marine fields. However, the mobility of underwater targets, asynchronous reception of localization information, stratification effects, and unknown measurement losses make UASN-assisted mobile target localization difficult. To address these issues, this study proposes a robust multi-model mobile target localization scheme (RMML) based on UASNs. The RMML suggests a simplified approach for optimal localization reference node selection based on the Cramér-Rao lower bound. This approach improves the localization accuracy of a mobile target by periodically selecting nodes that provide high-quality localization references. Using the localization reference provided by the selected nodes, a robust mobile target localization algorithm is developed based on an interacting multiple model and unscented Kalman filter. In this algorithm, a multipoint prediction method and ray tracing method are jointly proposed to improve the target state estimation accuracy under the asynchronous reception of localization information and the stratification effect. Maximum a posteriori probability estimation is used to estimate measurement loss, and pseudo-residuals are constructed based on the estimated measurement loss to improve the localization accuracy and robustness of the algorithm. Finally, extensive simulations and experiments are performed to verify the effectiveness of the RMML.

A Robust Semi-Direct 3D SLAM for Mobile Robot Based on Dense Optical Flow in Dynamic Scenes.

Dynamic objects bring about a large number of error accumulations in pose estimation of mobile robots in dynamic scenes, and result in the failure to build a map that is consistent with the surrounding environment. Along these lines, this paper presents a robust semi-direct 3D simultaneous localization and mapping (SLAM) algorithm for mobile robots based on dense optical flow. First, a preliminary estimation of the robot's pose is conducted using the sparse direct method and the homography matrix is utilized to compensate for the current frame image to reduce the image deformation caused by rotation during the robot's motion. Then, by calculating the dense optical flow field of two adjacent frames and segmenting the dynamic region in the scene based on the dynamic threshold, the local map points projected within the dynamic regions are eliminated. On this basis, the robot's pose is optimized by minimizing the reprojection error. Moreover, a high-performance keyframe selection strategy is developed, and keyframes are inserted when the robot's pose is successfully tracked. Meanwhile, feature points are extracted and matched to the keyframes for subsequent optimization and mapping. Considering that the direct method is subject to tracking failure in practical application scenarios, the feature points and map points of keyframes are employed in robot relocation. Finally, all keyframes and map points are used as optimization variables for global bundle adjustment (BA) optimization, so as to construct a globally consistent 3D dense octree map. A series of simulations and experiments demonstrate the superior performance of the proposed algorithm.

LSVL: Large-scale season-invariant visual localization for UAVs

Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in security applications, robust localization algorithms independent of GNSS are needed to provide dependable operations of autonomous UAVs also in interfered conditions. Typical non-GNSS visual localization approaches rely on known starting pose, work only on a small-sized map, or require known flight paths before a mission starts. We consider the problem of localization with no information on initial pose or planned flight path. We propose a solution for global visual localization on large maps, based on matching orthoprojected UAV images to satellite imagery using learned season-invariant descriptors, and test with environment sizes up to 100 km2. We show that the method is able to determine heading, latitude and longitude of the UAV at 12.6–18.7 m lateral translation error in as few as 23.2–44.4 updates from an uninformed initialization, also in situations of significant seasonal appearance difference (winter–summer) between the UAV image and the map. We evaluate the characteristics of multiple neural network architectures for generating the descriptors, and likelihood estimation methods that are able to provide fast convergence and low localization error. We also evaluate the operation of the algorithm using real UAV data and evaluate running time on a real-time embedded platform. We believe this is the first work that is able to recover the pose of an UAV at this scale and rate of convergence, while allowing significant seasonal difference between camera observations and map.

A robust cooperative localization algorithm based on covariance intersection method for multi-robot systems.

Cooperative localization is an arising research problem for multi-robot system, especially for the scenarios that need to reduce the communication load of base stations. This article proposes a novel cooperative localization algorithm, which can achieve high accuracy localization by using the relative measurements among robots. To address uncertainty in the measuring robots' positions and avoid linearization errors in the extended Kalman filter during the measurement update phase, a particle-based approximation method is proposed. The covariance intersection method is then employed to fuse preliminary estimations from different robots, guaranteeing a minimum upper bound for the fused covariance. Moreover, in order to avoid the negative effect of abnormal measurements, this article adopts the Kullback-Leibler divergence to calculate the distances between different estimations and rejects to fuse the preliminary estimations far from the estimation obtained in the prediction stage. Two simulations are conducted to validate the proposed algorithm. Compared with the other three algorithms, the proposed algorithm can achieve higher localization accuracy and deal with the abnormal measurement.

Road Shape Classification-Based Matching Between Lane Detection and HD Map for Robust Localization of Autonomous Cars

Many map matching-based localization algorithms estimate the autonomous car's pose using a registration between lanes measured by a camera and the High-Definition (HD) map. However, the registration methods based on numerical optimization, such as the Iterative Closest Point (ICP) and Normal Distributions Transform (NDT), can cause underdetermined problems due to no unique solution when matching under-constrained lane shapes such as straight lines and circular arcs. This paper proposes a robust localization algorithm with centimeter-level accuracy by applying different matching techniques depending on the road shape. We proposed a road shape classification-based map matching algorithm to overcome the under-constrained problems, which have no unique solution due to insufficient constraints. The proposed algorithm classifies lane segments into line, arc, and clothoid curves considering their curvature characteristics. After that, we find correction information through a map matching and covariance estimation method using lane pairs with the same type of their shape. For the under-constrained shapes, the geometry-based map-matching algorithm and covariance estimation method are applied to avoid underdetermined results. Finally, the measurement calculated from the correction information and predicted pose of the egovehicle is exploited for the measurement update of the Extended Kalman Filter (EKF). The proposed method was quantitatively evaluated in the simulation environment, which contains various road shapes, and qualitatively validated for experimental data from an autonomous driving platform. The proposed algorithm shows more robust matching capability, efficient computation time, and higher accuracy than the localization system based on ICP-based lane matching.

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 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 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.

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.

Unmanned ground vehicle‐unmanned aerial vehicle relative navigation robust adaptive localization algorithm

AbstractThe unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi‐platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV‐UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV‐UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)‐follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non‐linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV‐UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust‐EKF and Robust‐Adaptive‐EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non‐Gaussian distribution. The results show that under the non‐Gaussian distribution conditions, the accuracy of the Robust‐Adaptive‐EKF algorithm is improved by about two to three times compared with the EKF and Robust‐EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self‐adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness.

Two-Dimensional-Simultaneous Localisation and Mapping Study Based on Factor Graph Elimination Optimisation

A robust multi-sensor fusion simultaneous localization and mapping (SLAM) algorithm for complex road surfaces is proposed to improve recognition accuracy and reduce system memory occupation, aiming to enhance the computational efficiency of light detection and ranging in complex environments. First, a weighted signed distance function (W-SDF) map-based SLAM method is proposed. It uses a W-SDF map to capture the environment with less accuracy than the raster size but with high localization accuracy. The Levenberg–Marquardt method is used to solve the scan-matching problem in laser SLAM; it effectively alleviates the limitations of the Gaussian–Newton method that may lead to insufficient local accuracy, and reduces localisation errors. Second, ground constraint factors are added to the factor graph, and a multi-sensor fusion localisation algorithm is proposed based on factor graph elimination optimisation. A sliding window is added to the chain factor graph model to retain the historical state information within the window and avoid high-dimensional matrix operations. An elimination algorithm is introduced to transform the factor graph into a Bayesian network to marginalize the historical states and reduce the matrix dimensionality, thereby improving the algorithm localisation accuracy and reducing the memory occupation. Finally, the proposed algorithm is compared and validated with two traditional algorithms based on an unmanned cart. Experiments show that the proposed algorithm reduces memory consumption and improves localisation accuracy compared to the Hector algorithm and Cartographer algorithm, has good performance in terms of accuracy, reliability and computational efficiency in complex pavement environments, and is better utilised in practical environments.

Modified Factorized Transient Wave Model in Tree Pipe Networks for Leak Localization With Less Boundary Measurements

AbstractPressure measurement within each boundary pipe of a tree network has been the prerequisite to the formulation of its factorized transient wave propagation model, which is essential for developing a robust and efficient leak localization algorithm. However, the accessibility of all boundary pipes cannot be always guaranteed in underground pipe networks. The inaccessibility of any one boundary pipe for pressure sensor placement would result in the complete invalidity of this elegant algorithm. To address this problem, this paper reformulates and modifies the factorized wave propagation model, which allows the leak localization algorithm to be implemented without the strict accessibility requirement of all boundary pipes. This is realized by linearizing and factorizing the point transfer matrices of boundary pipes based on the small leak approximation. Numerical experiments demonstrate that the modified algorithm can accurately localize a single leak and give a good estimation of multiple co‐existing leaks in a tree network. Moreover, the modified algorithm is verified by recent laboratory experiments conducted in a tree network of viscoelastic pipes, where a leak is successfully localized.

Robust Sequential Fusion Estimation Based on Adaptive Innovation Event-Triggered Mechanism for Uncertain Networked Systems.

In order to reduce the transmission pressure of the networked system and improve its robust performance, an adaptive innovation event-triggered mechanism is designed for the first time, and based on this mechanism, the robust local filtering algorithm for the multi-sensor networked system with uncertain noise variances and correlated noises is presented. To avoid calculating the complex error cross-covariance matrices, applying the sequential fusion idea, the robust sequential covariance intersection (SCI) and sequential inverse covariance intersection (SICI) fusion estimation algorithms are proposed, and their robustness is analyzed. Finally, it is verified in the simulation example that the proposed adaptive innovation event-triggered mechanism can reduce the communication burden, the robust local filtering algorithm is effective for the uncertainty generated by the unknown noise variances, and two robust sequential fusion estimators show good robustness, respectively.

Local response estimation of a seagoing vessel using onboard measurement data

Structural integrity management is gaining popularity because of the wide-spreading application of the digital twin concept, which is based on the fusion of the real-world measurement data and digital model used for the design. Data fusion between the measurement data and design model makes structural integrity management a feasible solution with a limited number of sensors, provided a reliable and robust local response estimation algorithm can be provided. This paper presents a methodology through which a local stress response at an arbitrary location within a seagoing vessel can be estimated using onboard measurement data. For the response transfer from the measured location A to the unmeasured location B, the relative RAO, which defines the ratio of response amplitude between location A to B, was introduced. The relative RAO concept was applied by decomposing the response spectrum at measured location A into its comprising directional components using the least square method with non-parametric modeling. The proposed method was validated using the numerically generated pseudo measurement data, where some noises were introduced artificially to imitate the actual measurement. Finally, the full-scale measurement data of the 13,000TEU container carrier was analyzed and used to verify the proposed method.

A Robust 2D Lidar SLAM Method in Complex Environment

The two-dimensional (2D) lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the environment. We propose a robust 2D lidar simultaneous localization and mapping (SLAM) algorithm working in ambiguous environments. To improve the front-end scan-matching module’s accuracy and robustness, we propose performing degeneration analysis, line landmark tracking, and environment coverage analysis. The max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous environment. Moreover, we propose a tightly coupled framework that integrates lidar, wheel odometer, and inertial measurement unit (IMU). The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM problem. The experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8% of the existing method. And it can run stably in ambiguous environments where the existing method will be failed. Moreover, it ccan successfully construct a map with an area of more than 250 000 square meters.

A cascaded algorithm based on GMM, GKF, and data fusion for mobile tracking in wireless sensor network

SummaryWireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms.