Topological Map Research Articles

An Improved VSLAM for Mobile Robot Localization in Corridor Environment

Localization is a fundamental capability for an autonomous mobile robot, especially in the navigation process. The commonly used laser-based simultaneous localization and mapping (SLAM) method can build a grid map of the indoor environment and realize localization task. However, when a robot comes to a long corridor where there exists many geometrically symmetrical and similar structures, it often fails to position itself. Besides, the environment is not represented to a semantic level that the robot cannot interact well with users. To solve these crucial issues, in this paper, we propose an improved visual SLAM approach to realize a robust and precise global localization. The system is divided into two main steps. The first step is to construct a topological semantic map using visual SLAM, text detection and recognition, and laser sensor data. The second step is the localization which repeats part work of the first step but makes the best use of the prebuilt semantic map. Experiments show that our approach and solutions perform well and localize successfully almost everywhere in the corridor environment while traditional methods fail.

A self-organizing map approach for constrained multi-objective optimization problems

There exist many multi-objective optimization problems (MOPs) containing several inequality and equality constraints in practical applications, which are known as CMOPs. CMOPs pose great challenges for existing multi-objective evolutionary algorithms (MOEAs) since the difficulty in balancing the objective minimization and constraint satisfaction. Without loss of generality, the distribution of the Pareto set for a continuous m-objective CMOP can be regarded as a piecewise continuous manifold of dimension (m − 1). According to this property, a self-organizing map (SOM) approach for constrained multi-objective optimization problems is proposed in this article. In the proposed approach, we adopt the strategy of two population evolution, in which one population is evolved by considering all the constraints and the other population is used to assist in exploring the areas. In the evolutionary stage, each population is assigned a self-organizing map for discovering the population distribution structure in the decision space. After the topological mapping, we utilize the extracted neighborhood relationship information to generate promising offspring solutions. Afterwards, the neuron weight vectors of SOM are updated by the objective vectors of the surviving offsprings. Through the proposed approach, we can make the population efficiently converge to the feasible region with suitable levels of diversity. In the experiments, we compare the proposed method with several state-of-the-art approaches by using 48 benchmark problems. The evaluation results indicate that the overwhelmingly superior performance of the proposed method over the other peer algorithms on most of the tested problems. The source code is available at https://github.com/hccccc92918/CMOSMA.

Analysis of routing and security issues in OLSR protocol for video streaming over MANET

Mobile ad-hoc networks (MANETs) are autonomous, self-organizing networks without infrastructure support. The nodes in MANET network are dynamic and these nodes create creates a topological map through exchange of routing information defined by some set of rules called routing protocols which are further classified as proactive, reactive and hybrid. OLSR is proactive type of protocol which is widely used in MANET. The basic principal of OLSR is based on selection of Multipoint Relays (MPR) through flooding mechanism. There have been many enhancements in OLSR routing protocol to improve routing and security issues. But there is still a very vital scope for enhancement in OLSR to overcome these issues. In this paper, we review the different enhancements in OLSR and attempt to find out different routing and security challenges that can be further enhanced in OLSR for video streaming over MANET

MULTI-RESOLUTION REPRESENTATION USING GRAPH DATABASE

Abstract. Multi-resolution representation has always been an important and popular data source for many research and applications, such as navigation, land cover, map generation, media event forecasting, etc. With one spatial object represented by distinct geometries at different resolutions, multi-resolution representation is high in complexity. Most of the current approaches for storing and retrieving multi-resolution representation are either complicated in structure, or time consuming in traversal and query. In addition, supports on direct navigation between different representations are still intricate in most of the paradigms, especially in topological map sets. To address this problem, we propose a novel approach for storing, querying, and extracting multi-resolution representation. The development of this approach is based on Neo4j, a graph database platform that is famous for its powerful query and advanced flexibility. Benefited from the intuitiveness of the proposed database structure, direct navigation between representations of one spatial object, and between groups of representations at adjacent resolutions are both available. On top of this, collaborating with the self-designed web-based interface, queries within the proposed approach truly embraced the concept of keyword search, which lower the barrier between novice users and complicate queries. In all, the proposed system demonstrates the potential of managing multi-resolution representation data through the graph database and could be a time-saver for related processes.

Mapping Topology of Skyrmions and Fractional Quantum Hall Droplets to Nuclear EFT for Ultra-Dense Baryonic Matter

We describe the mapping at high density of topological structure of baryonic matter to a nuclear effective field theory that implements hidden symmetries emergent from strong nuclear correlations. The theory constructed is found to be consistent with no conflicts with the presently available observations in both normal nuclear matter and compact-star matter. The hidden symmetries involved are “local flavor symmetry” of the vector mesons identified to be (Seiberg-)dual to the gluons of QCD and hidden “quantum scale symmetry” with an IR fixed point with a “genuine dilaton (GD)” characterized by non-vanishing pion and dilaton decay constants. Both the skyrmion topology for Nf≥2 baryons and the fractional quantum Hall (FQH) droplet topology for Nf=1 baryons are unified in the “homogeneous/hidden” Wess–Zumino term in the hidden local symmetry (HLS) Lagrangian. The possible indispensable role of the FQH droplets in going beyond the density regime of compact stars approaching scale-chiral restoration is explored by moving toward the limit where both the dilaton and the pion go massless.

EXPLAINABILITY OF NEURAL NETWORK CLUSTERING IN INTERPRETING THE COVID-19 EMERGENCY DATA

Among other hospitalization causes and cases, the clinical emergency is a critical case and the data of the reporting patients are biased as well as poorly managed due to the chaotic situation. The world has faced chaos over the past year due to the frequent waves of COVID-19 and the resulting emergencies. The data banks, linked with the clinical emergencies require serious quantitative and qualitative analysis to drive interpretable conclusions for necessary future emergency measures and to develop explainable artificial intelligence tools. This important procedure involves the clear understanding of the data patterns and topologies, which is a great challenge for the multidimensional data sets. Mathematically, the topological mapping can resolve this problem by mapping higher-dimensional data to two-dimensional representation, based on the overall association. Proper data mining and pattern recognition can help in improving the rapid patients admission, in providing the medical resources timely and in proper patient administration. In this paper, the importance of self-organizing maps, to interpret the hospital data, particularly for the COVID-19 epidemic is discussed in detail. Important variables are identified with the aid of networks and mappings.

An Action Recognition Technology for Badminton Players Using Deep Learning

With the fast development of sports in recent years, the number of people participating in various sports has increased day by day. Because of the advantages of fewer constraints on the field and ease of learning, badminton became one of the most popular sports among them. Numerous works have been done specifically for the recognition of action of badminton players to improve and popularize it, but the traditional badminton player’s badminton action recognition algorithm employs the method of manually constructing a topology map to model the action sequence contained in multiple video frames. Besides, it learns each video frame in a targeted manner to reflect data changes, which is prone to high computational cost, low network generalization, and catastrophic forgetting. In response to the above problems, this paper proposes a deep learning-based action recognition technology for badminton players, which re-encodes the human hitting action sequence data with multirelational characteristics into relational triples, and learns by decoupling based on long short-term memory network. At the same time, this paper designs and completes a set of badminton action recognition schemes based on acceleration and angular velocity signals. Experimental results show that the proposed method achieves 63%, 84%, and 92%, respectively, recognition accuracy on multiple benchmark datasets, which improves the accuracy of human hitting action recognition. As a result, the evaluation will be useful in future work to improve the structure of current deep learning models for higher results in badminton action recognition.

Research on an Improved SOM Model for Damage Identification of Concrete Structures

In order to solve the problem of intelligent detection of damage of modern concrete structures under complex constraints, an improved self-organizing mapping (SOM) neural network model algorithm was proposed to construct an accurate identification model of concrete structure damage. Based on the structure and algorithm of the SOM network model, the whole process of the core construction of the concrete structure damage identification network model is summarized. Combined with the damage texture characteristics of concrete structures, through the self-developed 3D laser scanning system, an improved method based on a small number of samples to effectively improve the effectiveness of network input samples is proposed. Based on the principle of network topology map analysis and its image characteristics, a SOM model improvement method that can effectively improve the accuracy of the network identification model is studied. In addition, based on the reactive powder concrete bending fatigue loading test, the feasibility and accuracy of the improved method are verified. The results show that the improved SOM concrete structure damage identification model can effectively identify unknown neuron categories in a limited sample space, and the identification accuracy of the SOM network model is improved by 4.69%. The proposed improved SOM model method fully combines the network topology and its unique image features and can accurately identify structural damage. This research contributes to the realization of high-precision intelligent health monitoring of damage to modern concrete structures. In addition, it is of great significance for the timely detection, identification and localization of early damage to structures.

Quantitative Relations between Topological Similarity Degree and Map Scale Change of Contour Clusters in Multi-Scale Map Spaces

Quantitative relations between topological similarity degree and map scale change of multi-scale contour clusters are vital to the automation of map generalization. However, no method has been proposed to calculate the relations. This paper aims at filling the gap by proposing a new approach. It firstly constructed a directed contour tree by pre-processing of unclosed contours, and then developed a quantitative expression of topological relations of contour cluster based on directed contour tree. After this, it employed 108 groups of multi-scale contour clusters with different geomorphological types to explore the changing regularity of topological indices with map scale. Last, it used 416 points to calculate the quantitative relations between topological similarity degree and map scale change by curve fitting method. The results show that the quantitative expression of multi-scale topological indexes is closely related to the contour interval change, and power function is the best fit among the candidate functions.

Fast Path Planning of Autonomous Vehicles in 3D Environments

Three dimensional path planner is crucial for the safe navigation of autonomous vehicles (AV), such as unmanned aerial vehicles or unmanned underwater vehicles, which operate in three dimensions. In this paper, we develop a novel 3D path planner, which is fast in generating a near-optimal solution path. The planner generates the 3D path considering the size of an AV so that as the AV traverses the constructed path, it does not collide with an obstacle. This paper introduces a 3D path planner with novel concepts, such as a virtual agent and virtual sensors. In order to generate a 3D path to the goal as fast as possible, we let the virtual agent deploy virtual sensors iteratively, such that the connected sensor network can be formed. The constructed sensor network serves as a topological map for the AV, and we find a shortest path from the start to the goal utilizing the network. The virtual agent’s maneuver is biased towards the goal, in order to find a path to the goal as fast as possible. Moreover, the size of the agent is set considering the safety margin of the generated path. Through MATLAB simulations, we demonstrate the outperformance (low computational load and short path length) of our 3D path planner by comparing it with the 3D RRT-star algorithm.

Topology of the Shigella flexneri Enterobacterial Common Antigen polymerase WzyE

Enterobacteriales have evolved a specialized outer membrane polysaccharide [Enterobacterial Common Antigen (ECA)] which allows them to persist in various environmental niches. Biosynthesis of ECA initiates on the cytoplasmic leaflet of the inner membrane (IM) where glycosyltransferases assemble ECA repeat units (RUs). Complete RUs are then translocated across the IM and assembled into polymers by ECA-specific homologues of the Wzy-dependent pathway. Consisting of the membrane proteins Wzx, Wzy and Wzz, the Wzy-dependent pathway is the most common polysaccharide biosynthetic pathway in Gram-negative bacteria where it is most notably involved in LPS O antigen (Oag) biosynthesis. As such, the majority of research directed towards these proteins has been orientated towards Oag biosynthetic homologues with little directed towards ECA homologues. Belonging to the Shape, Elongation, Division and Sporulation (SEDS) protein family, Wzy proteins are polymerases, and are characterized as possessing little or no peptide homology among homologues as well as being polytopic membrane proteins with functionally relevant residues within periplasmic loops, as defined by C-terminal reporter fusion topology mapping. Here, we present the first the first major study into the ECA polymerase WzyE. Multiple sequence alignments and topology mapping showed that WzyE is unlike WzyB proteins involved with Oag biosynthesis WzyE displays high peptide conservation across Enterobacteriales. In silico structures and reporter mapping allowed us to identify possible functionally conserved residues with WzyESF's periplasmic loops, which we showed were crucial for its function. This work provides novel insight into Wzy proteins and suggests that WzyE is an optimal model to investigate Wzy proteins and the Wzy-dependent pathway.

Topological Semantic Mapping by Consolidation of Deep Visual Features

Many works in the recent literature introduce semantic mapping methods that use CNNs (Convolutional Neural Networks) to recognize semantic properties in images. The types of properties (eg.: room size, place category, and objects) and their classes (eg.: kitchen and bathroom, for place category) are usually predefined and restricted to a specific task. Thus, all the visual data acquired and processed during the construction of the maps are lost and only the recognized semantic properties remain on the maps. In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN (GoogLeNet), from 2D images captured in multiple views of the environment as the robot operates, to create, through averages, consolidated representations of the visual features acquired in the regions covered by each topological node. These representations allow flexible recognition of semantic properties of the regions and use in other visual tasks. Experiments with a real-world indoor dataset showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results.

SC-LPR: Spatiotemporal context based LiDAR place recognition

• A spatiotemporal and semantic information fusion based LiDAR place recognition method improves recognition accuracy. • A novel feature encoding module is proposed to effectively aggregate spatiotemporal features. • Spatiotemporal information is fused without geometrical transformations between LiDAR scans. • A superior feature similarity evaluation network is proposed to predict similarity score. 3D LiDAR-based place recognition remains challenging due to ambiguities and dynamics of scenes. Place representation can be learned effectively by deep learning, but perceptual confusion is inevitable based on single-scan. To alleviate the problem, we propose a new place recognition method named SC-LPR. In this method, spatiotemporal contextual information from LiDAR scans is used to increase the capacity of features’ representation. Firstly, a semantic graph is constructed to represent the topological geometric map of each LiDAR scan. Then, an end-to-end network is designed to predict similarity, in which GRU-EdgeConv++ is proposed to learn discriminative spatiotemporal feature representation and a novel C&TNT to predict a score. We evaluate our approach on the KITTI odometry benchmark. The experimental results show that our method can effectively fuse spatiotemporal information for place recognition. Compared with the state-of-the-art methods, our approach is superior on the KITTI dataset and can achieve competitive performance. To benefit the community by serving as a benchmark for place recognition, the code of our method will be made open-source on Github 1 1 https://github.com/Daideyun/SC-LPR .

Bioactive Components of Houttuynia cordata Thunb and Their Potential Mechanisms Against COVID-19 Using Network Pharmacology and Molecular Docking Approaches.

We investigated the molecular mechanism by which Houttuynia cordata Thunb (HCT) may intervene in coronavirus disease 2019 (COVID-19) and COVID-19-induced cytokine storms using network pharmacology and molecular docking approaches. Using the Traditional Chinese medicine Systems Pharmacology Database and Analysis Platform (TCMSP), a "component-target-pathway" topology map of HCT for COVID-19 treatment was constructed using Cytoscape. Core target genes were analyzed using the STRING database, and the signal pathway map and biological mechanism of COVID-19 therapy were obtained using cluster profilers. Active components of HCT were docked with severe respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp) using AutoDockTools. Data visualization and statistical analysis were conducted using the R program. A molecular dynamic simulation was carried out with the Groningen Machine for Chemical Simulation program. HCT had six active anti-COVID-19 ingredients and 45 molecular targets. Their crucial target proteins for COVID-19 treatment were the RELA (nuclear factor kappa B [NF-κB] p65 subunit), interleukin 6, and mitogen-activated protein kinase 1. In functional enrichment analysis, the potential molecular targets of active components of HCT for COVID-19 treatment belonged to 18 signaling pathways (adjusted P = 2.12E-11). Gene ontology obtained by Kyoto Encyclopedia of Genes and Genome enrichment screening showed that the primary mechanism of COVID-19 treatment was upregulation of protein kinase C followed by downregulations of T cell differentiation and proliferation and NF-κB signaling. Molecular docking showed that the active components of HCT (quercetin and kaempferol) had similar binding affinities for SARS-CoV-2 3CLpro and SARS-CoV-2 RdRp, primary COVID-19 target proteins as did clinically used drugs. These results were confirmed with molecular dynamics simulation. In conclusion, multiple components of HCT, especially quercetin and kaempferol, have the potential to treat COVID-19 infection and COVID-19-induced cytokine storm by targeting multiple proteins.

FNUG: Imperfect Mazes Traversal Based on Detecting and Following the Nearest-to-Final-Goal and Unvisited Gaps

Most of the traditional information-based unknown maze traversal techniques are based on trial and error, which are time-consuming and may trap the robot in infinite loops or dead ends. To address the above issues, this letter proposes a gap based approach for imperfect-unknown mazes traversal named Follow the Nearest-to-final goal and Unvisited Gap (FNUG) for the first time. The algorithm is computationally efficient, and has the ability to deal with loops and dead ends through the revisit check. The main part of this approach is finding out gaps around the robot by analyzing the depth scans, building a coordinate system based topological map in the form of a tree. Then a heuristic selection of the robot’s unvisited-neighbor gap for navigation based on its Euclidean distance from the final target location. Finally Dijkstra’s algorithm is used to find a path from robot’s location to the sub goal. Compared to existing sensor information-based methods, our proposed algorithm not only takes into account the current sensor data, the shape and size of the robot, but also all the sub goals visited so far. This is the key factor in this algorithm that prevents the robot from falling into a dead end or trapped in a loop. To prove the effectiveness of this algorithm, simulations and physical experiments were performed using using a 2-wheeled differential mobile robot.

An Occupancy Information Grid Model for Path Planning of Intelligent Robots

Commonly used robot map models include occupancy grid maps, topological maps, and semantic maps. Among these, an occupancy grid map is mainly represented as a quadrilateral grid. This paper proposes an occupancy information grid for intelligent robots by exploiting the advantages of the occupancy grid map and spatial information grid. In terms of geometric structure, a regular hexagonal grid is used instead of a regular quadrilateral grid. In terms of attribute structure, the single obstacle attribute is replaced by the grid terrain characteristics, grid element attributes, and grid edge attributes. Thus, the occupancy information grid model is transformed into a new data structure describing the spatial environment, and it can be effectively applied to map construction and path planning of intelligent robots. For the map construction application of intelligent robots, this paper describes the basic process of laser sensor-based grid model construction. For the path planning application of intelligent robots, this paper extends the A* algorithm based on a regular hexagonal grid. Additionally, map construction and path planning applications for intelligent robots are experimentally verified. Several experimental results were obtained. First, the experimental results confirmed the theoretical conclusion that the minimum sampling density of the hexagonal structure was 13.4% lower than that of the quadrilateral structure. Second, the regular hexagonal grid is clearly more advantageous in describing environmental scenes, which can ameliorate the "undercompleteness" phenomenon. Third, there were large differences in the planning paths based on two types of grids, as shown by the fact that the distance of the planning paths obtained by the regular hexagonal grid was reduced by at least 10.8% and at most 15.6% compared with the regular quadrilateral grid.

Research and Application of Intelligent Layout Design Algorithm for 3D Pipeline of Nuclear Power Plant

The pipeline layout design of nuclear power plant is to find the optimal route to meet the objectives and constraints in the 3D routing space. However, due to the intensive equipment and complex structure of the nuclear power plant, various types of pipeline systems, complex layout constraints, and a large number of pipelines, even for experienced designers, pipeline layout is a difficult and time-consuming task. In order to solve the problem of the automatic layout of pipeline in 3D routing space of nuclear power plant, a pipeline automatic routing method combining Dijkstra algorithm in large space and improved A∗algorithm in local space is proposed in this paper. Firstly, the method identifies the key vertices of each room in the nuclear power plant, constructs the topological routing map, and determines the preliminary passage area of the pipeline through the traditional Dijkstra algorithm. Secondly, the space of the layout area is divided into 3D grids, and then the items in the area are identified and preprocessed. Finally, the 3D pipeline routing environment is established through AABB-OBB hybrid collision detection technology. On this basis, the improved method of A∗evaluation function is given to satisfy the pipeline layout constraints and improve the search efficiency. Through experiments, the effectiveness of this method is proved. This method can quickly and automatically route the nuclear power pipelines that meet the requirements of the engineering, which greatly improves the efficiency of 3D pipeline layout design for the nuclear power plant.

Topological entropy of the extended tree map on the space of fuzzy numbers

In this paper, we study the connection between topological entropies of general tree maps and their extended maps on the spaces of fuzzy numbers. First, we show some aspects of the structures of ω-limit sets of the extended (and induced) topological tree maps. Next, we prove that topological entropies of a crisp tree map and its extended map all agree.

Object-Based Reliable Visual Navigation for Mobile Robot.

Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training to improve their generality. To improve the reliability of visual navigation, in this paper, we propose a novel object-level topological visual navigation method. Firstly, a lightweight object-level topological semantic map is constructed to release the dependence on the precise metric map, where the semantic associations between objects are stored via graph memory and topological organization is performed. Then, we propose an object-based heuristic graph search method to select the global topological path with the optimal and shortest characteristics. Furthermore, to reduce the global cumulative error, a global path segmentation strategy is proposed to divide the global topological path on the basis of active visual perception and object guidance. Finally, to achieve adaptive smooth trajectory generation, a Bernstein polynomial-based smooth trajectory refinement method is proposed by transforming trajectory generation into a nonlinear planning problem, achieving smooth multi-segment continuous navigation. Experimental results demonstrate the feasibility and efficiency of our method on both simulation and real-world scenarios. The proposed method also obtains better navigation success rate (SR) and success weighted by inverse path length (SPL) than the state-of-the-art methods.

A Hierarchical Path Planning Approach with Multi-SARSA Based on Topological Map.

In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method.