Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (15)
  • Open Access


    RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

    Lieping Zhang1,2, Xiaoxu Shi1,2, Liu Tang1,2, Yilin Wang3, Jiansheng Peng4, Jianchu Zou4,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2111-2136, 2024, DOI:10.32604/cmc.2024.047235

    Abstract A Rapid-exploration Random Tree (RRT) autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping (SLAM) algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot. Firstly, an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward, which introduces the reference value of guide nodes’ deflection probability into the random sampling function so that the global search tree can detect frontier… More >

  • Open Access


    Dynamic SLAM Visual Odometry Based on Instance Segmentation: A Comprehensive Review

    Jiansheng Peng1,2,*, Qing Yang1, Dunhua Chen1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 167-196, 2024, DOI:10.32604/cmc.2023.041900

    Abstract Dynamic Simultaneous Localization and Mapping (SLAM) in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving. However, in the face of complex real-world environments, current dynamic SLAM systems struggle to achieve precise localization and map construction. With the advancement of deep learning, there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years, and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM. Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic… More >

  • Open Access


    Visual SLAM Based on Object Detection Network: A Review

    Jiansheng Peng1,2,*, Dunhua Chen1, Qing Yang1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3209-3236, 2023, DOI:10.32604/cmc.2023.041898

    Abstract Visual simultaneous localization and mapping (SLAM) is crucial in robotics and autonomous driving. However, traditional visual SLAM faces challenges in dynamic environments. To address this issue, researchers have proposed semantic SLAM, which combines object detection, semantic segmentation, instance segmentation, and visual SLAM. Despite the growing body of literature on semantic SLAM, there is currently a lack of comprehensive research on the integration of object detection and visual SLAM. Therefore, this study aims to gather information from multiple databases and review relevant literature using specific keywords. It focuses on visual SLAM based on object detection, covering different aspects. Firstly, it discusses… More >

  • Open Access


    RO-SLAM: A Robust SLAM for Unmanned Aerial Vehicles in a Dynamic Environment

    Jingtong Peng*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2275-2291, 2023, DOI:10.32604/csse.2023.039272

    Abstract When applied to Unmanned Aerial Vehicles (UAVs), existing Simultaneous Localization and Mapping (SLAM) algorithms are constrained by several factors, notably the interference of dynamic outdoor objects, the limited computing performance of UAVs, and the holes caused by dynamic objects removal in the map. We proposed a new SLAM system for UAVs in dynamic environments to solve these problems based on ORB-SLAM2. We have improved the Pyramid Scene Parsing Network (PSPNet) using Depthwise Separable Convolution to reduce the model parameters. We also incorporated an auxiliary loss function to supervise the hidden layer to enhance accuracy. Then we used the improved PSPNet… More >

  • Open Access


    Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

    Gengyu Ge1,2,*, Zhong Qin1, Xin Chen1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6351-6369, 2023, DOI:10.32604/cmc.2023.035832

    Abstract Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The laser SLAM-based method usually uses… More >

  • Open Access


    Feature Matching Combining Variable Velocity Model with Reverse Optical Flow

    Chang Zhao1, Wei Sun1,3,*, Xiaorui Zhang2,3, Xiaozheng He4, Jun Zuo1, Wei Zhao1

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1083-1094, 2023, DOI:10.32604/csse.2023.032786

    Abstract The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion, which leads to a false matching, with an inaccurate pose estimation or failed tracking. To address the challenge above, a new method of feature point matching is proposed in this paper, which combines the variable velocity model with the reverse optical flow method. First, the constant velocity model is extended to a new variable velocity model, and the expanded variable velocity model is used to provide the initial pixel shifting for… More >

  • Open Access


    Intelligent SLAM Algorithm Fusing Low-Cost Sensors at Risk of Building Collapses

    Dahyeon Kim, Junho Ahn*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1657-1671, 2023, DOI:10.32604/cmc.2023.029216

    Abstract When firefighters search inside a building that is at risk of collapse due to abandonment or disasters such as fire, they use old architectural drawings or a simple monitoring method involving a video device attached to a robot. However, using these methods, the disaster situation inside a building at risk of collapse is difficult to detect and identify. Therefore, we investigate the generation of digital maps for a disaster site to accurately analyze internal situations. In this study, a robot combined with a low-cost camera and two-dimensional light detection and ranging (2D-lidar) traverses across a floor to estimate the location… More >

  • Open Access


    Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

    Tingting Yang1,*, Shuwen Jia1, Ying Yu1, Zhiyong Sui2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1691-1704, 2023, DOI:10.32604/iasc.2023.027466

    Abstract Augmented Reality (AR) tries to seamlessly integrate virtual content into the real world of the user. Ideally, the virtual content would behave exactly like real objects. This necessitates a correct and precise estimation of the user’s viewpoint (or that of a camera) with regard to the virtual content’s coordinate system. Therefore, the real-time establishment of 3-dimension (3D) maps in real scenes is particularly important for augmented reality technology. So in this paper, we integrate Simultaneous Localization and Mapping (SLAM) technology into augmented reality. Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm. In… More >

  • Open Access


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

    Naigong Yu*, Hejie Yu

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 1147-1169, 2022, DOI:10.32604/cmes.2022.019430

    Abstract 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… More >

  • Open Access


    Strategy for Creating AR Applications in Static and Dynamic Environments Using SLAM- and Marker Detector-Based Tracking

    Chanho Park1,2, Hyunwoo Cho1, Sangheon Park1, Sung-Uk Jung1, Suwon Lee3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 529-549, 2022, DOI:10.32604/cmes.2022.019214

    Abstract Recently, simultaneous localization and mapping (SLAM) has received considerable attention in augmented reality (AR) libraries and applications. Although the assumption of scene rigidity is common in most visual SLAMs, this assumption limits the possibilities of AR applications in various real-world environments. In this paper, we propose a new tracking system that integrates SLAM with a marker detection module for real-time AR applications in static and dynamic environments. Because the proposed system assumes that the marker is movable, SLAM performs tracking and mapping of the static scene except for the marker, and the marker detector estimates the 3-dimensional pose of the… More >

Displaying 1-10 on page 1 of 15. Per Page