Improve Localization Accuracy Research Articles

SLM-SLAM: a visual SLAM system based on segmented large-scale model in dynamic scenes and zero-shot conditions

In practical applications, the existence of diverse dynamic objects can compromise the localization precision of most conventional Visual Simultaneous Localization and Mapping (VSLAM) systems. Simultaneously, many dynamic VSLAM systems based on neural networks require pre-training for specific application scenarios. We introduce SLM-SLAM, the first VSLAM system that implements zero-shot processing of dynamic scenes. It achieves the capability to handle various dynamic objects without the necessity for pre-training, enabling straightforward adaptation to different application scenarios. Firstly, we designed an open-world semantic segmentation module based on a segmented large-scale model to acquire semantic information in the scene. Subsequently, we devised a label-based strategy for selecting feature points, jointly optimizing poses with the weighted labels provided by both semantic and geometric information. Finally, we refined the keyframe selection strategy of ORB-SLAM3 to prevent matching errors caused by an insufficient number of remaining static feature points in the scene. We conducted experiments on the TUM dataset, the KITTI dataset, and real-world scenarios. The results indicate that in dynamic scenes, our SLM-SLAM significantly improves localization accuracy compared to ORB-SLAM3, and its performance is comparable to state-of-the-art dynamic VSLAM systems.

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.

An efficient estimator for source localization in WSNs using RSSD and TDOA measurements

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature.

Evaluating habitat-specific interference in automated radio telemetry systems: implications for animal movement studies

Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of 1 week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats.

Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization

ObjectiveHigh frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm’s parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. MethodsWe detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. ResultsSOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. ConclusionsSOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SignificanceVisual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.

DeforT: Deformable transformer for visual tracking

Most trackers formulate visual tracking as common classification and regression (i.e., bounding box regression) tasks. Correlation features that are computed through depth-wise convolution or channel-wise multiplication operations are input into both the classification and regression branches for inference. However, this matching computation with the linear correlation method tends to lose semantic features and obtain only a local optimum. Moreover, these trackers use an unreliable ranking based on the classification score and the intersection over union (IoU) loss for the regression training, thus degrading the tracking performance. In this paper, we introduce a deformable transformer model, which effectively computes the correlation features of the training and search sets. A new loss called the quality-aware focal loss (QAFL) is used to train the classification network; it efficiently alleviates the inconsistency between the classification and localization quality predictions. We use a new regression loss called α-GIoU to train the regression network, and it effectively improves localization accuracy. To further improve the tracker’s robustness, the candidate object location is predicted by using a combination of online learning scores with a transformer-assisted framework and classification scores. An extensive experiment on six testing datasets demonstrates the effectiveness of our method. In particular, the proposed method attains a success score of 71.7% on the OTB-2015 dataset and an AUC score of 67.3% on the NFS30 dataset, respectively.

Improving weakly-supervised object localization using adversarial erasing and pseudo label

Weakly-supervised learning approaches have gained significant attention due to their ability to reduce the effort required for human annotations in training neural networks. This paper investigates a framework for weakly-supervised object localization, which aims to train a neural network capable of predicting both the object class and its location using only images and their image-level class labels. The proposed framework consists of a shared feature extractor, a classifier, and a localizer. The localizer predicts pixel-level class probabilities, while the classifier predicts the object class at the image level. Since image-level class labels are insufficient for training the localizer, weakly-supervised object localization methods often encounter challenges in accurately localizing the entire object region. To address this issue, the proposed method incorporates adversarial erasing and pseudo labels to improve localization accuracy. Specifically, novel losses are designed to utilize adversarially erased foreground features and adversarially erased feature maps, reducing dependence on the most discriminative region. Additionally, the proposed method employs pseudo labels to suppress activation values in the background while increasing them in the foreground. The proposed method is applied to two backbone networks (MobileNetV1 and InceptionV3) and is evaluated on three publicly available datasets (ILSVRC-2012, CUB-200-2011, and PASCAL VOC 2012). The experimental results demonstrate that the proposed method outperforms previous state-of-the-art methods across all evaluated metrics.

Optimizing lightning location accuracy: A study of propagation velocity and time of arrival in long-range lightning location algorithms

The propagation velocity (v) and time of arrival (Ta) are the primary factors affecting long-range lightning location. In this paper, we have enhanced the Very Low Frequency Long-range Lightning Location Network (VLF-LLN) in China in terms of both pulse pairing methods and location algorithms. Then, the algorithm utilized in the VLF-LLN, and four other long-range lightning location algorithms were compared using lightning strike accidents, simulated lightning strike, and other lightning data as the reference values. Building on this foundation, the discussion centers on the effect of v and Ta on location accuracy, with the results showing that an appropriate Ta yields a more significant improvement in location accuracy compared to v. VLF-LLN uses the peak time of the ground wave as Ta avoids the significant lag caused by sky wave, which is present in other algorithms, and therefore has the best location accuracy with a median location accuracy of 1.81 km.

Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

MRPSO-Loc: Multi-targets UHF RFID localization system based on MRPSO algorithm

Existing RFID localization techniques mainly focus on the location of a single target, which do not fully utilize the multi-targets identification merit of UHF RFID systems. In this paper, a multi-targets localization algorithm called merged re-shaken particle swarm optimization (MRPSO) is proposed. The ranging information and the angle information, which are obtained by using multi-frequency phase information with the help of frequency hopping technique, are merged together during the iteration process of MRPSO. In addition, based on the traditional PSO algorithm, a low weight particle filtering method was proposed, and a re-shaken scheme was introduced to improve localization accuracy. By allocating particles to each target, MRPSO can obtain each target location simultaneously after PSO process, which avoids time consuming. The experimental results show that MRPSO can achieve single-station multi-target localization with a median accuracy of 9.64 cm.

Impact monitoring of large size complex metal structures based on sparse sensor array and transfer learning

During aircraft operations, the impact events experienced by the aircraft may cause damage to the structure, thus posing a safety hazard. Therefore, an accurate determination of where the impact occurred and the time history of the impact force can provide an important basis for assessing the condition of the aircraft. However, modern aircraft structures are often large and complex, and relying on dense arrays of sensors for monitoring adds additional weight to the aircraft and reduces the economics of aircraft operation. This paper proposes a region-to-point monitoring strategy. First, a Convolutional Neural Network (CNN) model with region localization capability is trained using the sparse sensor array acquisition data. Then, the weighted center algorithm is used to determine the specific location where the impact occurs, in which the added fuzzy genetic algorithm can provide the ability to adjust weights to improve localization accuracy adaptively. As for the impact force prediction, this paper adopts a model based on a Convolutional Neural Network-Gated Recurrent Unit combined with a Squeeze-Excitation attention mechanism (CNN-GRU-SE), which is capable of predicting the impact force occurring in the flat plate and reinforced structure region of the aircraft under different energy conditions. Finally, the impact of incorporating a transfer learning approach on model performance and training cost is investigated for fuselage regions with different structures.

Inertial Algorithm with Dry Fraction and Convolutional Sparse Coding for 3D Localization with Light Field Microscopy

Light field microscopy is a high-speed 3D imaging technique that records the light field from multiple angles by the microlens array(MLA), thus allowing us to obtain information about the light source from a single image only. For the fundamental problem of neuron localization, we improve the method of combining depth-dependent dictionary with sparse coding in this paper. In order to obtain higher localization accuracy and good noise immunity, we propose an inertial proximal gradient acceleration algorithm with dry friction, Fast-IPGDF. By preventing falling into a local minimum, our algorithm achieves better convergence and converges quite fast, which improves the speed and accuracy of obtaining the locolization of the light source based on the matching depth of epipolar plane images (EPI). We demonstrate the effectiveness of the algorithm for localizing non-scattered fluorescent beads in both noisy and non-noisy environments. The experimental results show that our method can achieve simultaneous localization of multiple point sources and effective localization in noisy environments. Compared to existing studies, our method shows significant improvements in both localization accuracy and speed.

Energy-efficient mobile node localization using CVA technology and SAI algorithm

In the evolving landscape of the Internet of Things (IoT), Mobile Wireless Sensor Networks (MWSN) play a pivotal role, particularly in dynamic environments requiring mobile sensing capabilities. A primary challenge in MWSNs is achieving accurate node positioning with minimal energy consumption, as these networks often consist of battery-powered, mobile sensors where energy replenishment is difficult. This paper addresses the critical problem of energy-efficient node localization in MWSNs. We propose a novel positioning approach leveraging Cooperative Virtual Array (CVA) technology, which strategically utilizes the mobility of nodes to enhance positioning accuracy while conservatively using energy resources. The methodology revolves around optimizing the number of transceiver nodes, considering factors such as node moving speed, total energy consumption, and positioning errors. Central to our approach is the Signal Arrival and Interaction (SAI) algorithm, an innovative technique devised for efficient and precise mobile node localization, replacing traditional Time of Arrival (ToA) methods. Our simulations, conducted under various scenarios, demonstrate the significant advantages of the CVA-based positioning algorithm. Results show a marked reduction in energy consumption and robust performance in mobile node scenarios. Key findings include substantial improvements in localization accuracy and energy efficiency, highlighting the potential of our approach in enhancing the operational sustainability of MWSNs. The implications of this research are far-reaching for IoT applications, particularly those involving mobile sensors, such as in smart cities, industrial monitoring, and disaster management. By introducing a novel, energy-efficient positioning method, our work contributes to the advancement of MWSN technology, offering a sustainable solution to the challenge of mobile node localization.

Digital-twin-assisted meta learning for soft-failure localization in ROADM-based optical networks

Reconfigurable optical add/drop multiplexer (ROADM) nodes are evolving towards high-degree architectures to support growing traffic and enable flexible network connectivity. Due to the complex composition of high-degree ROADMs, soft failures may occur between both inter- and intra-node components, like wavelength selective switches and fiber spans. The intricate ROADM structure significantly contributes to the challenge of localizing inter-/intra-node soft failures in ROADM-based optical networks. Machine learning (ML) has shown to be a promising solution to the problem of soft-failure localization, enabling network operators to take accurate and swift measures to overcome such challenges. However, data scarcity is a main hindrance when using ML for soft-failure localization, especially in the complex scenario of inter- and intra-node soft failures. In this work, we propose a digital-twin-assisted meta-learning framework to localize inter-/intra-node soft failures with limited samples. In our proposed framework, we construct several mirror models using a digital twin of the physical optical network and then generate multiple training tasks. These training tasks serve as pretraining data for the meta learner. Then, we use real data for fine-tuning and testing of the meta learner. The proposed framework is compared with the rule-based reasoning method, transfer-learning-based method, and artificial-neural-network-based method with no pretraining. Experimental results indicate that the proposed framework improves localization accuracy by over 15%, 33%, and 54%, on average, compared to benchmark approaches, respectively.

Multi-frequency probabilistic imaging fusion for impact localization on aircraft composite structures

Since the internal barely visible damage of aircraft composite structures caused by the impact is a critical problem, impact monitoring is essential for the integrity and reliability of aircraft composite structures. This paper presents a multi-frequency probabilistic imaging fusion method for localizing impacts on aircraft composite structures. To capture the impact signals, a network of distributed sensors is mounted on the structure. The impact signals are then processed using the continuous wavelet transform (CWT) to extract the multi-frequency narrowband Lamb wave signals. The time difference of arrival (TDOA), a key feature of the impact source, is measured using averaging techniques employed in the normalized variance sequence. Subsequently, a probabilistic imaging function is established, and the TDOA of narrowband Lamb wave signals at each frequency is used as the feature input to generate the multi-frequency probabilistic imaging results. To determine the performance of the imaging results at each frequency, an efficiency index is introduced, allowing for the retention or abandonment of the imaging results. By utilizing the retained multi-frequency probabilistic imaging results, the proposed method achieves impact localization through imaging fusion. Experimental verification is conducted on a stiffened aircraft composite panel, and a comparison is made with two existing methods: the hyperbolic locus imaging method and the virtual time reversal imaging method. The results show that the proposed method can significantly improve localization accuracy compared to the existing methods, and is effective even in the presence of measurement noise.

SiamRAAN: Siamese Residual Attentional Aggregation Network for Visual Object Tracking

The Siamese network-based tracker calculates object templates and search images independently, and the template features are not updated online when performing object tracking. Adapting to interference scenarios with performance-guaranteed tracking accuracy when background clutter, illumination variation or partial occlusion occurs in the search area is a challenging task. To effectively address the issue with the abovementioned interference and to improve location accuracy, this paper devises a Siamese residual attentional aggregation network framework for self-adaptive feature implicit updating. First, SiamRAAN introduces Self-RAAN into the backbone network by applying residual self-attention to extract effective objective features. Then, we introduce Cross-RAAN to update the template features online by focusing on the high-relevance parts in the feature extraction process of both the object template and search image. Finally, a multilevel feature fusion module is introduced to fuse the RAAN-enhanced feature information and improve the network’s ability to perceive key features. Extensive experiments conducted on benchmark datasets (GOT-10K, LaSOT, OTB-50, OTB-100 and UAV123) demonstrated that our SiamRAAN delivers excellent performance and runs at 51 FPS in various challenging object tracking tasks. Code is available at https://github.com/MallowYi/SiamRAAN.

Visual–Inertial Odometry of Structured and Unstructured Lines Based on Vanishing Points in Indoor Environments

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method.

Outdoor LiDAR-inertial SLAM using ground constraints

AbstractThe current LiDAR-inertial odometry is prone to cumulative Z-axis error when it runs for a long time. This error can easily lead to the failure to detect the loop-closing in the correct scenario. In this paper, a ground-constrained LiDAR-inertial SLAM is proposed to solve this problem. Reasonable constraints on the ground motion of the mobile robot are incorporated to limit the Z-axis drift error. At the same time, considering the influence of initial positioning error on navigation, a keyframe selection strategy is designed to effectively improve the flatness and accuracy of positioning and the efficiency of loop detection. If GNSS is available, the GNSS factor is added to eliminate the cumulative error of the trajectory. Finally, a large number of experiments are carried out on the self-developed robot platform to verify the effectiveness of the algorithm. The results show that this method can effectively improve location accuracy in outdoor environments, especially in environments of feature degradation and large scale.

Processing‐in‐memory based multilateration localization in wireless sensor networks using memristor crossbar arrays

SummaryMost of the multilateration algorithms for object localization rely heavily on matrix multiplication for calculating the precise location of the target from all the reference points in a 3D domain. A significant disadvantage is that performing matrix multiplication can be computationally expensive, particularly for larger matrices, leading to slower performance and longer processing times. In this article, we propose a Processing‐in‐Memory (PIM) based localization approach for multilateration using a memristor crossbar array for performing faster matrix multiplication. The experiments were conducted on real‐time data collected through ultra‐wideband (UWB) chip. The simulation results demonstrate that the proposed model is around 50% faster than the fastest known localization algorithm considered for evaluation and exhibits an average of 55% improvement in localization accuracy.

Co-Operatively Increasing Smoothing and Mapping Based on Switching Function

Collaborative localization is a technique that utilizes the exchange of information between multiple sensors or devices to improve localization accuracy and robustness. It has a wide range of applications in autonomous driving and unmanned aerial vehicles (UAVs). In the field of UAVs, collaborative localization can help UAVs perform autonomous navigation and mission execution in complex environments. However, when GNSS is not available, it becomes challenging to position the UAV swarm relative to each other. This is because the swarm loses its perception and constraint of the position relationship between each member. Consequently, the swarm faces the problem of the serious drift of relative accuracy for an extended period. Furthermore, when the environment is faced with complex obstruction challenges or a camera with low texture scenes, noise can make it more difficult to solve the relative position relationship between drones, and a single UAV may lose positioning capability. To solve these specific problems, this paper studies a swarm co-operative localization method in a GNSS-denied environment with loud noise interference. In this paper, we proposed a method that utilizes a distributed scheme based on an incremental smoothing and mapping (iSAM) algorithm for state estimation. It incorporates new anchor-free topological constraints to prevent positioning failures and significantly improve the system’s robustness. Additionally, a new switching function is applied in front of each factor of the loss function, which adjusts the switches in real time in response to the input information to improve observably the accuracy of the system. A novel co-operative incremental smoothing and mapping (CI-SAM) method is proposed and the method does not require a complete relative position measurement, which reduces the need for vehicle measurement equipment configuration. The effectiveness of the method is verified by simulation.