Map Construction Method Research Articles

SRCLoc: Synthetic Radio Map Construction Method for Fingerprinting Outdoor Localization in Hybrid Networks

A precise localization system is a key enabling technology for Internet of Things (IoT) applications and location-based services. Fingerprint-based localization methods are well-known and widely used solutions. These methods, however, are time-consuming and laborious for radio map construction during an offline site survey in large-scale applications. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network-based radio map construction method for real-time device localization. The proposed synthetic radio map construction method for fingerprinting outdoor localization (SRCLoc) combined the hybrid support vector machine and deep gated recurrent unit algorithms sequentially. The SRCLoc reduced the workload of site surveying required to build the fingerprint database by up to 85.7%. The results show that the average positioning error of SRCLoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Construction of a Knowledge Map Based on Text CNN Algorithm for Maritime English Subjects

Knowledge map is a new method of knowledge management with the information revolution. This paper is aimed at forming a systematic and standardized huge redundant knowledge structure, which can be used to mine the knowledge structure and the relationship between knowledge and visualize it in a graphical way, in order to obtain more representative information and improve the classification accuracy of text classification model. In this paper, a knowledge map construction method based on the Text CNN algorithm is proposed for the subject of Nautical English. It is of practical significance and academic value to make use of knowledge map to study Chinese Maritime English, which is helpful to the development of Chinese Maritime English and provides guidance. In order to maintain the diversity of particle swarm optimization, the Text CNN algorithm is combined with the construction of Maritime English subject knowledge map, and the network parameters and structure are optimized. Using knowledge map to study China Maritime English has important practical significance and academic value and has certain guiding significance for the development of China Maritime English.

CRCLoc: A Crowdsourcing-Based Radio Map Construction Method for WiFi Fingerprinting Localization

The WiFi-based fingerprint indoor-positioning system has attracted increasing interest from industry and academia, benefiting from the widespread deployment of the wireless local area network (WLAN) infrastructure. However, with the expansion of application scenarios, this system suffers from labor-intensive work for received signal strength (RSS) fingerprint construction. In this article, we propose a crowdsourcing-based radio map construction method and trajectory matching algorithm to solve the time-consuming preliminary fingerprint data collection in the offline phase, where the tedious collection work is replaced by the massive crowdsourcing sensor data. First, the latent information of the map is extracted by using some image processing methods, and all possible routes are obtained simultaneously with the depth-first traversal method. Then, considering the strict restrictions on walking in indoor environments, the crowdsourcing trajectories are produced by matching the result of pedestrian dead reckoning (PDR) with the candidate routes based on the Shape Context algorithm. Further, a crowdsourcing trajectory can be represented by uniformly distributed reference points based on step detection, and each point corresponds to a unique RSS of access points (APs). Since such a fingerprint construction method can be performed while smartphone holders are moving and not aware of their actual positions, it can be referred to the dynamic construction method of the radio map. The real scenario experiments reveal that the proposed solution can significantly reduce the time and manpower consumption to build the radio map. Moreover, compared with traditional schemes, our crowdsourcing-based radio map construction method for the WiFi fingerprinting localization (CRCLoc) system can improve the accuracy and robustness of the indoor-positioning results.

A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions

Deep learning has achieved significant advances in the fault diagnosis of rotating machinery. However, it still suffers many challenges such as various working conditions, large environmental noise interference and insufficient effective data samples. Signal time–frequency analysis and feature transfer learning methods can help solve these problems. Combining wavelet packet transform (WPT) and multi-kernel maximum mean discrepancy (MK-MMD), this paper proposes a novel residual network (ResNet)-based deep transfer diagnosis model for bearing faults. Firstly, this paper devises a distinctive WPT time–frequency feature map (WPT-TFFM) construction method using WPT for time–frequency analysis on nonlinear and non-stationary vibration signals. Then, a modified multi-group parallel ResNet network is structured to extract the depth features of WPT-TFFM for the characteristics of small size and feature dispersion. Then, MK-MMD is further applied to evaluate the distribution difference between the depth features of the source and target domain data. Combining with the classification loss of the sample set with the source domain, the depth features extraction network is optimized to achieve better cross-domain invariance and fault state differentiation capability of the depth features. To evaluate the proposed method, this work conducts comparative experiments on two test rigs under different working loads and speeds. The results reveal that the proposed method offers excellent fault diagnosis and noise prevention capability for working condition transfer tasks.

THE RELATIONSHIP BETWEEN MIND MAPS AND LEARNING APPROACHES OF MATHEMATICS AND ACCOUNTING TEACHERS TAKING PEDAGOGICAL FORMATION EDUCATION

Learning approaches are thought to be related to the attitude of individuals towards learning. Mind maps, on the other hand, can be considered as visual tools that enable learning when looking at the definitions in the literature. The problem of this research is whether the methods preferred by individuals while creating mind maps and the quality of the mind maps they create are shaped according to their learning approaches. In the research, it was aimed to reveal the relationships between the learning approaches of pre-service mathematics and accounting teachers who received pedagogical formation education and the department they study in, and some variables (mind map score, mind map construction method) based on the mind maps they prepared. The research was carried out in a survey design, which is one of the quantitative research methods. The study group of the research consists of 59 (21 mathematics, 38 accounting) teacher candidates who took the "Special Teaching Methods" course in the pedagogical formation education program. For the content of the course, an explanation was given to the teacher candidates about how to prepare mind maps and what to consider when preparing mind maps. The first of the two measurement tools used in the research is the mind maps prepared by the pre-service teachers about special teaching methods. The second one is the "Learning Approaches Scale" developed by Biggs et al. (2001) and adapted to Turkish by Batı et al. (2010) and ensured its validity and reliability. The scale was applied online to 59 pre-service teachers who constituted the study group of the research. Analysis of the obtained data was carried out with Independent T-Test and One-Way Analysis of Variance (ANOVA). In the findings of the analysis, it was revealed that both mathematics and accounting teacher candidates were inclined to the deep learning approach. At the same time, no significant difference was found between the learning approaches of the pre-service teachers and the department they studied, their mind map scores and mind map making methods. Finally, a general evaluation was made for the findings and suggestions were made for the studies to be done in this field.

Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion

In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized.

A Map Construction Method Based on the Cognitive Mechanism of Rat Brain Hippocampus

The entorhinal-hippocampus structure in the mammalian brain is the core area for realizing spatial cognition. However, the visual perception and loop detection methods in the current biomimetic robot navigation model still rely on traditional visual SLAM schemes and lack the process of exploring and applying biological visual methods. Based on this, we propose a map construction method that mimics the entorhinal-hippocampal cognitive mechanism of the rat brain according to the response of entorhinal cortex neurons to eye saccades in recent related studies. That is, when mammals are free to watch the scene, the entorhinal cortex neurons will encode the saccade position of the eyeball to realize the episodic memory function. The characteristics of this model are as follows: 1) A scene memory algorithm that relies on visual saccade vectors is constructed to imitate the biological brain's memory of environmental situation information matches the current scene information with the memory; 2) According to the information transmission mechanism formed by the hippocampus and the activation theory of spatial cells, a localization model based on the grid cells of the entorhinal cortex and the place cells of the hippocampus was constructed; 3) Finally, the scene memory algorithm is used to correct the errors of the positioning model and complete the process of constructing the cognitive map. The model was subjected to simulation experiments on publicly available datasets and physical experiments using a mobile robot platform to verify the environmental adaptability and robustness of the algorithm. The algorithm will provide a basis for further research into bionic robot navigation.

Retracted] Video Tactical Intelligence Analysis Method of Karate Competition Based on Convolutional Neural Network

The performance of image classification technology based on deep network has been greatly improved, making computer vision enter the stage of industrialization and be gradually applied to many aspects of human work and life. As a typical classification task in computer vision, human behavior recognition has immeasurable potential value in medical, family, transportation, and other scenarios. At the same time, in the field of competitive sports, the integration of artificial intelligence technology and sports technical and tactical analysis is undoubtedly an important way to innovate and improve the technical and tactical level. Taking karate as an example, the study of athletes’ training and competition videos is an important means and method for technical and tactical analysis in competitive sports. Traditional tactical intelligence analysis methods have many shortcomings, such as high labor cost, serious data loss, long delay, and low accuracy. Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes’ technical action recognition, action frequency statistics, and trajectory tracking. The technology effectively makes up for the disadvantages of traditional tactical intelligence analysis methods. The research results show that the new topology map construction method has a significant effect on improving the accuracy of behavior recognition and also lays a foundation for technical and tactical analysis.

Multi-Scale Exposure Fusion Based on Multi-Visual Feature Measurement and Detail Enhancement Representation

Multi-exposure image fusion (MEF) is used to generate a high-quality image from a series of images with different exposure levels. The multi-scale-based MEF method achieves better fusion performance than single-scale-based method because it can better preserve the global contrast information. However, this method still has the problem of the loss of details in the fused results. To solve this problem, a novel multi-scale exposure image fusion method based on multi-visual feature measurement and detail enhancement representation in the IHS color space is proposed. First, three visual features of the source multi-exposure images, namely contrast, saturation, and exposure, are measured and are then adopted to construct the initial weight maps with adaptive weighting coefficients. Second, to optimize the initial weight maps and obtain the middle weight maps, a decision map construction method is proposed by comparing the pixel values of the detail maps of the intensity components, which can enhance the representation of detail information. Third, guided filtering is applied to eliminate the noise in the middle weight maps to obtain the final weight maps, which improves the visual effect of the fused image. Finally, image pyramid decomposition and reconstruction are performed on the source images and the final weight maps to achieve the final fused image. Numerical experimental results indicate that the proposed method outperforms state-of-the-art methods in terms of subjective visual and quantitative evaluations.

Method for Fast Map Construction Based on GPS Data and Compressed Grid Algorithm

Electronic maps play an important role in the field of urban traffic management, but the interface functions provided by map service agencies are limited, and commercial maps are generally expensive. Furthermore, the map generation algorithms based on the Global Positioning System (GPS) data can be very complex and take up a lot of storage space, which limits their application to specific practical problems, such as the real-time update of area maps, temporary road control, emergency route planning, and other scenarios. In order to solve this problem, an intuitive, extensible, and flexible method of constructing urban road maps is proposed. Using the Othello-coordinated method, the representation of the unit grid cell was redesigned. Through this method, the disadvantages of the raster map’s large storage space and computing resource requirements are compensated for during processing, improving the topological expression ability of the raster map and the speed with which the construction of the map is realized. The application potential of the proposed method is demonstrated by the evaluation of public transport service and road network resilience. In our experiments, the optimization efficiency of storage space was up to 99.914%, and the calculation accuracy of bus coverage was about 99.86%.

Construction and Application of Prediction Spectrum between PEMFC’s Performances and Ship Vibration

In order to analyze and evaluate the influence of ship vibration on the performance of proton exchange membrane fuel cell (PEMFC), firstly, the frequency domain and amplitude distribution characteristics of ship vibration are studied. Based on the general PEMFC simulation mathematical model, a boundary condition, loading method based on sliding grid technology (ZM-UDF) is proposed to establish the technical route of numerical simulation of PEMFC influence relationship on ship vibration performance. Secondly, in the full solution domain of ship vibration, a numerical simulation scheme is designed to study the influence of ship vibration direction, frequency and amplitude on the performance of proton exchange membrane fuel cell (PEMFC). Through simulation calculation, the performance database of ship vibration-proton exchange membrane fuel cell is constructed. Furthermore, the linear programming model of simulation data is established by using the difference- piecewise fitting method, and a prediction map construction method of ship vibration proton exchange membrane fuel cell performance characterization based on numerical simulation data is proposed. The performance prediction map of ship vibration proton exchange membrane fuel cell is drawn and verified by simulation.

The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination

Semantic information usually contains a description of the environment content, which enables mobile robot to understand the environment and improves its ability to interact with the environment. In high-level human–computer interaction application, the Simultaneous Localization and Mapping (SLAM) system not only needs higher accuracy and robustness, but also has the ability to construct a static semantic map of the environment. However, traditional visual SLAM lacks semantic information. Furthermore, in an actual scene, dynamic objects will reduce the system performance and also generate redundancy when constructing map. these all directly affect the robot’s ability to perceive and understand the surrounding environment. Based on ORB-SLAM3, this article proposes a new Algorithm that uses semantic information and the global dense optical flow as constraints to generate dynamic-static mask and eliminate dynamic objects. then, to further construct a static 3D semantic map under indoor dynamic environments, a fusion of 2D semantic information and 3D point cloud is carried out. the experimental results on different types of dataset sequences show that, compared with original ORB-SLAM3, both Absolute Pose Error (APE) and Relative Pose Error (RPE) have been ameliorated to varying degrees, especially on freiburg3-walking-xyz, the APE reduced by 97.78% from the original average value of 0.523, and RPE reduced by 52.33% from the original average value of 0.0193. Compared with DS-SLAM and DynaSLAM, our system improves real-time performance while ensuring accuracy and robustness. Meanwhile, the expected map with environmental semantic information is built, and the map redundancy caused by dynamic objects is successfully reduced. the test results in real scenes further demonstrate the effect of constructing static semantic maps and prove the effectiveness of our Algorithm.

Research on the Construction Method of 3D Dense Map Based on Visual Slam

Aiming at the problem that visual SLAM (Simultaneous Localization and Mapping) cannot satisfy both high-precision positioning and rapid mapping in large scenes, this paper proposes a map construction method. First, when calculating the pose, the method of combining EPNP and graph optimization is used to optimize the pose estimation to improve the accuracy of the pose. Then, the paper uses Lie algebra to define the vertices and edges of the pose graph, which improves the convergence speed of the model. Finally, when constructing a three-dimensional map, a divide-and-conquer map method is proposed, which uses multiple sub-maps that contain each other for point cloud registration and down-sampling filtering, so as to improve the accuracy of map construction. Experimental results show that the algorithm in this paper has good performance. In the TUM test set, the speed can reach 0. 139s/frame, which is about 0.2s higher than the original method, and the accuracy error RMSE value is reduced to 0.055m. The feasibility of the algorithm is proved, and it can meet the accuracy and speed requirements of 3D dense map construction in large scenes.

Slam algorithm for multi-robot communication in unknown environment based on particle filter

To solve the SLAM (simultaneous localization and map building) of mobile robots, a multi-robot SLAM algorithm based on particle filter for communication in unknown environment is proposed. In the standard particle filter, the incremental map construction method based on point-line consistency is introduced to preserve the hypothesis of the line segment feature map in each particle, and the importance function is introduced to the observation information. On this basis, sampling map fusion algorithm extracts ORB (oriented FAST and rotated BRIEF) features of local map constructed by a single robot and finds the optimal matching points, calculates the homography matrix of local map optimal matching point set, determines the optimal affine transformation parameters among point sets, so as to complete the local map fusion, and realize the multi-robot communication based on SLAM in unknown environment. The results show that the algorithm achieves high-precision map fusion and it is an effective SLAM construction algorithm for multiple machines.

Laser SLAM optimization based on resampling

In order to solve the particle degradation and particle exhaustion in the simultaneous positioning and map construction method of Rao-Blackwellized particle filter, an optimization method for simultaneous positioning and map construction is proposed. In order to alleviate the need for too much data in the re-sampling stage, the calculation time is longer, and the calculation amount is reduced by dropping some low-weight particles and reducing the number of particles. The experimental results show that compared with the traditional RBPF-SLAM method, the improved method can obtain a more accurate map with fewer particles, reduce the number of Kalman particle filtering, and reduce the amount of calculation to meet the needs of actual use.

Movement-aware map construction

ABSTRACT Map construction algorithms attempt to derive a spatial graph representing a road network from GPS-sampled movement trajectories. Existing methods commonly use trajectories without considering the specific sampling methodology. Hence, the movement information is not preserved in the map construction results. The proposed map-construction method considers the particularities of the sampling process and how they affect the trajectory data to improve the overall result quality. Specifically, our proposed algorithm constructs nodes by clustering turn points. We use an adaptive clustering approach that considers when a turn point was sampled in relation to the ‘true’ node location based on the trajectory geometry. As nodes are the aggregates of turn points, edges are constructed by conflating trajectories that either connect turn points or are in close proximity to inferred nodes. Experiments using trajectory datasets at different spatial scales, data complexities, and data sources in combination with several assessment methods show that the proposed movement-aware map construction method produces maps of greater accuracy than those from the existing approaches.

A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots

Although many studies exist on mobile robot path planning, the disadvantages of complex algorithms and many path nodes in logistics warehouses and manufacturing workshops are obvious, mainly due to the inconsistency of map environment construction and pathfinding strategies. In this study, to improve the efficiency of mobile robot path planning, the Delaunay triangulation algorithm was used to process complex obstacles and generate Voronoi points as pathfinding priority nodes. The concept of the grid was used to extract obstacle edges to provide obstacle avoidance strategies for robot pathfinding. Subsequently, the search for priority and regular path nodes used the improved A-star (A*) algorithm. The dynamic fusion pathfinding algorithm (DFPA), based on Delaunay triangulation and improved A*, was designed, which realizes the path planning of mobile robots. MATLAB 2016a was used as the simulation software, to firstly verify the correctness of the DFPA, and then to compare the algorithm with other methods. The results show that under the experimental environment with the same start point, goal point, and number of obstacles, the map construction method and pathfinding strategy proposed in this paper reduce the planned path length of the mobile robot, the number of path nodes, and the cost of overall turn consumption, and increase the success rate of obtaining a path. The new dynamic map construction method and pathfinding strategy have important reference significance for processing chaotic maps, promoting intelligent navigation, and site selection planning.

Space-Frequency-Interpolated Radio Map

This paper presents a novel method for radio map construction that simultaneously interpolates the received signal power values over space and frequency domains. Radio maps can be used to improve spectrum management and for localization systems, which are related to wireless systems in general such as cellular systems, Internet of Things (IoT) networks, and vehicular communications. Researchers have shown that crowdsourcing using spatial interpolation techniques can be used to accurately construct a radio map to improve these applications; however, because of the limitation in the spatial domain, conventional methods can build a radio map for only those frequencies at which sensing can be performed. Our proposed method focuses on the fact that the shadowing values show strong correlation over a wide range of frequency domains. The main idea is to treat the shadowing values obtained over various frequencies as the ones over target frequency. Using the actual datasets obtained over 870, 2115, and 3500 MHz in a cellular system, we show that the proposed method can accurately generate a radio map, even if no (or few) datasets are available in the target frequency.

A high efficient multi-robot simultaneous localization and mapping system using partial computing offloading assisted cloud point registration strategy

The robots using visual simultaneous localization and mapping (SLAM) system are generally experiencing excessive power consumption and suffer from depletion of battery energy during the course of working. The intensive computation necessary to complete complicated tasks is overwhelming for inexpensive mobile robots with limited on-board resources. To address this problem, a novel task offloading strategy combined with a new dense point cloud map construction method is proposed in this paper, which is firstly used for the improvement of the system especially in indoor scenes. First, we develop a novel strategy to remotely offload computation-intensive tasks to cloud center so that the tasks that could not originally be achieved locally on the resource-limited robot systems become possible. Second, a modified iterative closest point algorithm (ICP), named fitness score hierarchical ICP algorithm (FS-HICP), is developed to accelerate point cloud registration. The correctness, efficiency, and scalability of the proposed strategy are evaluated with both theoretical analysis and experimental simulations. The results show that the proposed method can effectively reduce the energy consumption while increase the computation capability and speed of the multi-robot visual SLAM system, especially in indoor environment.

Robust and baseline-free full-field defect detection in complex composite parts through weighted broadband energy mapping of mode-removed guided waves

In this study, a non-destructive testing approach is investigated for finding damages in fiber reinforced polymers using broadband piezoelectric excitation and scanning laser Doppler vibrometer measurements.An efficient broadband filter in wavenumber-frequency domain is considered, in order to calculate a mode-removed broadband weighted root mean square (WRMS) energy map of the guided waves. Compensation of the frequency-dependent wave attenuation is implemented. Because this energy map relates exclusively to abnormalities in the wave field, it shows high sensitivity to all kinds of internal damages. The proposed damage map construction method is automated, and does not require any a priori information on the inspected part or measurement conditions. Further, it is not limited to application on flat coupon samples, but can be equally applied on complex composite parts.The methods are tested on an academic case of a CFRP plate with flat bottom holes and on an industrial case of a large stiffened CFRP aircraft panel with distributed manufacturing defects. It is shown that the broadband mode-removed WRMS energy map is highly sensitive to various kinds of damages, even if they are small and deep inside the material.