Localization Algorithm Research Articles

Focus-SLAM: A visual Monocular SLAM

Abstract This research presents a novel simultaneous localization and mapping algorithm, called Focus-SLAM, which simulates human navigation strategies by synthesizing a optical saliency model, SalNavNet, in a traditional single-shot SLAM paradigm. SalNavNet introduces an innovative design that incorporates a correlation module and an adaptive Exponential Moving Average (EMA) component, effectively addressing the prevalent center bias issue found in contemporary saliency models. As a result, the system enhances target fixation by accentuating salient features more effectively. Extensive experimental evaluations, conducted across a spectrum of indoor environments exhibiting diverse luminosity conditions. These findings attest to the efficacy of Focus-SLAM in enhancing both the accuracy and efficiency of SLAM operations under challenging real-world conditions.

Trajectory optimization of near-Earth asteroids exploration by using reusable probes from cislunar space

Implementing the flyby to Near-Earth Asteroids (NEAs) with the potential impact risks to the Earth allows for obtaining detailed physical parameters, thereby supporting the high-precision orbit prediction and planetary defense strategy. Different from those conducted asteroid flyby missions, in the 12th China Trajectory Optimization Competition (CTOC-12), a NEAs flyby trajectory design problem using reusable probes that depart from a Lunar Distant Retrograde Orbit (DRO) station in the cislunar space was released. The objective was flyby to as many NEAs as possible using up to 20 probes within a total of 10 years. The ∑ team proposed a solution that can explore 47 NEAs using 11 probes, ranking the first in the competition. In this paper, the methods and results from the winning team are introduced, including mission analysis and preliminary design, and low-energy transfer trajectory optimization. In particular, a round-trip trajectory is divided into three phases: deep space transfer, indirect transfer between the Earth to DRO, and DRO phasing and rendezvous. With the combination of global optimization and local optimization algorithms, the required velocity increments to change the orbital planes are effectively reduced, thus increasing the number of the explored NEAs. The final solution of our team is presented and the results are compared with those of the top three teams. The competition demonstrates that the regularization of flyby missions from the cislunar space to explore NEAs with the potential impact risks to the Earth is the feasible and promising.

Recognition and quantification of apparent damage to concrete structure based on computer vision

To solve the problems of poor recognition of apparent concrete damage and the difficulty of efficient quantitative characterization of the damage parameters, this study proposes a damage detection method that integrates the target localization algorithm and the quantitative characterization algorithm of the damage based on computer vision. This paper proposes a YOLO v5-RS network combining Res2Net and SimAM to solve the problem of indistinctive apparent damage feature. The damage is segmented by image processing, and the crack and crush damage quantification method are proposed. The results show that compared with YOLO v5, the mAP and F1 of YOLO V5-RS are improved by 8.4% and 0.07, respectively, which indicates that the recognition of apparent structural damage has high robustness. To verify the recognition method’s generalization ability and the characterization method’s accuracy, four cases of different scale are introduced in this paper.

Sound source localization based on microphone array mounted on unmanned aerial vehicles

Abstract Microphone array installed on unmanned aerial vehicles (UAV) can be used for acoustic detection in scenes, such as detecting noise sources in factories or monitoring corona in power grids. However, using acoustic sensors on UAV has great challenges. The motor rotation of UAV and the non-stationary noise generated by propeller make it difficult for microphone array to obtain useful information for sound source localization. In order to reduce self-noise, an improved least mean square (LMS) adaptive filtering algorithm is proposed. Combining filtering algorithm and multiple signal classification (MUSIC) algorithm to locate the sound source, the noise spectrum characteristics of UAV in outdoor free flight mode are analyzed. The microphone array is used to collect data and realize sound source localization in low Signal-to-Noise Ratio (SNR) environment. The simulation and experimental results show that the algorithm improves the robustness of the sound source localization algorithm in low SNR environment, and realizes the real-time localization of the target sound source during the flight of UAV with microphone array.

A hybrid gradient climbing algorithm for a swarm robot-based gas leak detector

Methane emissions from leak sources can have a negative climate impact, in addition to contributing to the risk of explosions in urban environments. These risks can be minimized by developing systems that provide for an accurate and timely detection and localization of a gas leakage point. This research used a swarm of robots to detect and locate a leakage point. The localization algorithm derives from further optimization of the gradient climbing algorithm using fireflies acting as opportunistic agents. Firefly agents are characterized by their bioluminescent communication which guides them to dynamically adjust their positions and intensities based on the quality of the gradient information available to them. The proposed research focuses on enhancing gas leak detection through the development of a hybrid gradient climbing algorithm. This algorithm integrates gradient climbing techniques with swarm intelligence, utilizing the strengths of both approaches. This simulation resulted in the hybrid algorithm leading to a reduced convergence time and path lengths when compared to the swarm without opportunistic agents. The suggested approach can be important especially in gas distribution systems or in areas where human intervention is considered to be unsafe.

Gridless mixed-field sources localisation algorithm based on covariance matrix reconstruction

Gridless mixed-field sources localisation algorithm based on covariance matrix reconstruction

An Underwater Target Location Algorithm Combined with Schmidt Orthogonalization

Abstract In underwater target localization research, where challenges such as low signal-to-noise ratio and multiple interferences exist in conventional matched field processing, this paper presents a novel approach combining compressed sensing with Schmidt orthogonalization. Initially, an orthogonalization procedure is applied to the observation matrix, yielding a new matrix with reduced column coherence and maximal adherence to the Restricted Isometry Property (RIP) constraint. Subsequently, the Sparse Adaptive Matching Pursuit (SAMP) algorithm from compressed sensing is employed to reconstruct the signal, leading to precise underwater target localization. Simulation experiments substantiate that this combined approach outperforms the conventional matched field algorithm by yielding a higher signal-to-noise ratio for target acoustic source localization and markedly reducing the count of interference virtual sources. Consequently, the performance of localization for targets is substantially enhanced.

Research on single-phase grounding fault location technology in distribution networks based on impedance method

This article mainly introduces the impedance based single-phase grounding fault location method for distribution networks, including its theoretical basis, algorithm steps, and simulation verification. First, starting from the impedance analysis of the transmission line model, the method of accurately measuring the location of the fault point through phase domain analysis is explained. Next, the process of impedance analysis for single-phase grounding faults was described in detail, that is, how to solve the impedance of the grounding fault points by calculating the voltage and current signals. Then, the specific process of the impedance based grounding fault location algorithm was introduced, including the calculation of equivalent load impedance, the calculation of starting voltage and current, the calculation of grounding current, and the solution of fault point location. Finally, simulation verification was conducted using an IEEE 34 node distribution system example in a MATLAB/Simulink environment, and the results showed that the algorithm has high positioning accuracy, with a maximum error of within 3%.

Robust algorithms for spherical angle-of-arrival source localization

The performance of traditional algorithms for spherical angle-of-arrival (AOA) source localization will be significantly degraded when there are outliers in the angle measurements. By using the symmetric α-stable (SαS) distribution to describe the measurement noise containing outliers and constructing the cost function using the lp-norm, we propose a robust algorithm for spherical AOA source localization: the spherical iteratively reweighted pseudolinear estimator (SIRPLE). The SIRPLE is similar to the iteratively reweighted least squares (IRLS), with the difference that a homogeneous least squares (HLS) problem is solved in each iteration. The SIRPLE suffers from bias problems owing to the nature of the pseudolinear estimators. To overcome this problem, the instrumental variable (IV) method is introduced and the spherical iteratively reweighted instrumental variable estimator (SIRIVE) is proposed. Theoretical analysis shows that the SIRIVE is asymptotically unbiased and it can achieve the theoretical error covariance of the constrained least lp-norm estimation. Extensive simulation analyses demonstrate the better performance of the SIRIVE compared to the conventional spherical AOA source localization methods and the SIRPLE under SαS noise environment. The performance of the SIRIVE is similar to that of the Nelder–Mead algorithm (NM), but the SIRIVE are computationally more efficient. In addition, the SIRIVE is nearly unbiased and the root mean square error (RMSE) performance is close to the Cramér–Rao lower bound (CRLB).

Vessel recovery using ultrasound localisation microscopy: An in silico comparative study between minimum variance and delay-and-sum beamformers

The use of particle localisation and tracking algorithms on Contrast Enhanced Ultrasound (CEUS) or other ultrasound mode image data containing sparse microbubble (MB) populations, can produce super-resolved vascularization maps. Typically such data stem from conventional delay and sum (DAS) beamforming that is used widely in ultrasound imaging modes. Recently, adaptive beamforming has shown significant improvement in spatial resolution, but its value to super-resolution image analysis approaches is not fully understood. The in silico study here evaluates the performance of combining minimum variance beamformers (MV BF), established to provide improved lateral resolution, compared to DAS BFs with single particle detection. The isolated effect of a range of simplified image-affecting factors such as flow profile, pulse length, noise, vessel separations and data availability is considered. The study aims to assess the vessel recovery performance using the different beamformers and investigate the link with MB detection and localisation. The MV BF was shown to provide improved microvessel position accuracy compared to conventional DAS BFs. In particular, vessel separations between 0.3–4 λ provided superior localisation uncertainty with the MV. In addition, for a separation of 0.36λ, vessel recovery was achieved with both methods but the use of MV eliminated artifacts that appear as additional vessels. These results were found to be linked to improved MB detection and localisation for the MV BF, which is proposed as suitable for testing in Ultrasound Localisation Microscopy (ULM) imaging using patient data.

On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses

As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

A fast local search based memetic algorithm for the parallel row ordering problem

The parallel row ordering problem (PROP) is concerned with arranging two groups of facilities along two parallel lines with the goal of minimizing the sum of the flow cost-weighted distances between the pairs of facilities. As the main result of this paper, we show that the insertion neighborhood for the PROP can be explored in optimal time Θ(n2) by providing an O(n2)-time procedure for performing this task, where n is the number of facilities. As a case study, we incorporate this procedure in a memetic algorithm (MA) for solving the PROP. We report on numerical experiments that we conducted with MA on PROP instances with up to 500 facilities. The experimental results demonstrate that the MA is superior to the adaptive iterated local search algorithm and the parallel hyper heuristic method, which are state-of-the-art for the PROP. Remarkably, our algorithm improved best known solutions for six largest instances in the literature. We conjecture that the time complexity of exploring the interchange neighborhood for the PROP is Θ(n2), exactly as in the case of insertion operation.

Integration of dual band radio waves and ensemble-based approach for rice moisture content determination and localisation

Maintaining optimal moisture content in grain storage is critical to ensuring adequate supply throughout the year, but it presents a significant challenge. Current moisture measurement methods often necessitate sophisticated and costly equipment. This paper introduces an approach employing real-time rice moisture content determination and detection of spoilage (specifically wet spots) within a storage facility achieved through the utilisation of radio waves operating at 2.4 GHz and 868 MHz, along with an ensemble-based machine learning algorithm. Experimental samples spanning from 12% to 30% moisture levels were collected, then subjected to pre-processing, and subsequently employed to train the Ensemble-based Rice Moisture Content and Localisation (eRMCL) algorithm. The eRMCL produced an effective prediction of both rice moisture content and the localisation of wet spots within the grain storage unit. The results show that compared to support vector machine, random forest, and machine learning methods, the eRMCL algorithm had the best performance metrics, with an accuracy of 94.8% in predicting the moisture content and location of spoilage in storage. The measurement of moisture content and the identification of wet spots in rice storage using the dual frequency wave approach were found to be more accurate than with a single frequency band. Thus, the dual frequency band is a novel method for the determination of the moisture content of stored rice and the localisation of the spoilage area.

Analyzing the latent space of GAN through local dimension estimation for disentanglement evaluation

The impressive success of style-based GANs (StyleGANs) in high-fidelity image synthesis has motivated research to understand the semantic properties of their latent spaces. In this paper, we approach this problem through a geometric analysis of latent spaces as a manifold. In particular, we propose a local dimension estimation algorithm for arbitrary intermediate layers in a pre-trained GAN model. The estimated local dimension is interpreted as the number of possible semantic variations from this latent variable. Moreover, this intrinsic dimension estimation enables unsupervised evaluation of disentanglement for a latent space. Our proposed metric, called Distortion, measures an inconsistency of intrinsic tangent space on the learned latent space. Distortion is purely geometric and does not require any additional attribute information. Nevertheless, Distortion shows a high correlation with the global-basis-compatibility and supervised disentanglement score. Our work is the first step towards selecting the most disentangled latent space among various latent spaces in a GAN without attribute labels.

Target localization algorithm based on improved short-time Fourier transform

The properties of Doppler through-wall radar (TWR) in target localization applications mainly depend on the accuracy of instantaneous frequency (IF) estimations obtained from the time-frequency distribution (TFD) of target echo. However, when the time-frequency aliasing occurs in TFD, it is difficult to accurately estimate the target IF curves. To solve this problem, an improved short-Time Fourier transform is proposed in this paper. Firstly, a series of rough estimation time-frequency information (IF and chirp rate) of target echoes are obtained by fractional Fourier transform. Secondly, we update the time-frequency window according to the rough time-frequency information of the target signal. Meanwhile, we define a new distribution estimator based on the local maximum. Finally, the target IF curve is obtained by the local maximum operator as well as the updated window function. The experimental results show that the method significantly improves the target IF estimation and localization accuracy by 76.5 % and 74.6 %, respectively, compared with STFT.

An expandable and cost-effective data center network

With the rapid growth of data volume, the escalating complexity of data businesses, and the increasing reliance on the Internet for daily life and production, the scale of data centers is constantly expanding. The data center network (DCN) is a bridge connecting large-scale servers in data centers for large-scale distributed computing. How to build a DCN structure that is flexible and cost-effective, while maintaining its topological properties unchanged during network expansion has become a challenging issue. In this paper, we propose an expandable and cost-effective DCN, namely HHCube, which is based on the half hypercube structure. Further, we analyze some characteristics of HHCube, including connectivity, diameter, and bisection bandwidth of the HHCube. We also design an efficient algorithm to find the shortest path between any two distinct nodes and present a fault-tolerant routing scheme to obtain a fault-tolerant path between any two distinct fault-free nodes in HHCube. Meanwhile, we present two local diagnosis algorithms to determine the status of nodes in HHCube under the PMC model and MM* model, respectively. Our results demonstrate that despite the presence of up to 25% faulty nodes in HHCube, both algorithms achieve a correct diagnosis rate exceeding 90%. Finally, we compare HHCube with state-of-the-art DCNs including Fat-Tree, DCell, BCube, Ficonn, and HSDC, and the experimental results indicate that the HHCube is an excellent candidate for constructing expandable and cost-effective DCNs.

Variation-aware automated design and optimization of sub-1V bandgap voltage reference

A robust systematic gm/ID-based design procedure for a CMOS low-voltage bandgap reference is introduced. The proposed approach is technology node independent, and it eliminates invoking the simulator in the loop by using precomputed lookup tables (LUTs) generated once. The proposed methodology is capable of addressing the impact of PVT corners and random mismatch. The proposed procedure is verified against Spectre simulations and yields very accurate results. Moreover, the bandgap reference automated synthesis procedure is fully vectorized, enabling the concurrent synthesis of multiple design points in a short time. As a result, large datasets can be generated to span the whole design space, which enables global optimization. Next, local optimization algorithms can be utilized to quickly determine the degrees of freedom of an optimal design point that meets a set of specifications. The speedup of the proposed methodology is around 140x compared to simulation-based optimization in addition to accomplishing better results.

Adaptive robot localization in dynamic environments through self-learnt long-term 3D stable points segmentation

In field robotics, particularly in the agricultural sector, precise localization presents a challenge due to the constantly changing nature of the environment. Simultaneous Localization and Mapping algorithms can provide an effective estimation of a robot’s position, but their long-term performance may be impacted by false data associations. Additionally, alternative strategies such as the use of RTK-GPS can also have limitations, such as dependence on external infrastructure. To address these challenges, this paper introduces a novel stability scan filter. This filter can learn and infer the motion status of objects in the environment, allowing it to identify the most stable objects and use them as landmarks for robust robot localization in a continuously changing environment. The proposed method involves an unsupervised point-wise labelling of LiDAR frames by utilizing temporal observations of the environment, as well as a regression network, called Long-Term Stability Network (LTS-NET) to learn and infer 3D LiDAR points long-term motion status. Experiments demonstrate the ability of the stability scan filter to infer the motion stability of objects on a real agricultural long-term dataset. Results show that by only utilizing points belonging to long-term stable objects, the localization system exhibits reliable and robust localization performance for long-term missions compared to using the entire LiDAR frame points.

Fitness landscapes of buffer allocation problem for production lines with unreliable machines

We study the structural properties of the buffer allocation problem from the fitness landscape perspective. We consider manufacturing flow lines with series–parallel network structure. The machines are supposed to be unreliable, their time to failure and repair time are exponentially distributed. Tentative solutions are evaluated by means of an approximate method based on the Markov models aggregation. We carry out computational experiments with local search and genetic algorithms in order to evaluate the fitness landscape properties of previously published instances and their modifications. It turns out that the so-called ‘massif central’ or ‘big valley’ structure of the fitness landscape is present but only partially: The fitness of local optima is negatively correlated with the distance to the best found solution, yet the set of local optima cannot be encompassed by a ball of relatively small size with respect to the size of solution space. Moreover, we show that in many problem instances, several clusters of local optima can be identified. The performance of genetic algorithms is discussed with respect to population clustering and the permanent usage of crossover is recommended.