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检索条件"主题词=SLAM robotics"

共 184 条记录，以下是91-100 订阅

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A Real-Time Optical-Flow-based slam FPGA Accelerator with Inter-Frame Similarity Exploitation and Correlation-Guided Mixed-Precision Flow Update

A Real-Time Optical-Flow-based SLAM FPGA Accelerator with In...

引用

2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024

作者： Li, Mengjie Zhu, Haozhe Zhang, Yiming He, Siqi Liu, Qi Zeng, Xiaoyang Chen, Chixiao Fudan University Shanghai China

ISBN: (纸本)9798350376326

Recently, the increasing demands of scene understanding and precise spatial mapping have driven Simultaneous Localization and Mapping (slam) more crucial in robotics for estimating the trajectory and the structure of the environment [1-2]. Previous feature-based slam processors [3-5] retrieve pose and 3D maps by tracking limited feature points (30fps) tracking, as shown in Fig. 1: (1) The data dependency between feature extraction and correlation construction causes latency overhead. (2) Iterative optical flow updates require an excessive computational amount, accounting for 62.19% of the total computation. © 2024 IEEE.

关键词： slam robotics

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Implementing slam for Robot Navigation with Lidar in ROS Platform 4

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

引用

4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Verma, Gaurav Kumar Ray, Ranjit Swar, Surya Narayan Meena, Gagan Deep Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (纸本)9798331528614

This research covers the implementation of slam (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the slam. slam is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely. © 2024 IEEE.

关键词： slam robotics

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Design of an Adaptive Spiral-Loop Pipeline Robot Based on Laser slam 6

Design of an Adaptive Spiral-Loop Pipeline Robot Based on La...

引用

6th International Conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wang, Chengda School of Artificial Intelligence Anhui University Anhui Hefei China

ISBN: (纸本)9798331541699

Urban development leads to a large and complex underground pipe network with pipe issues. Regular inspections of pipelines are necessary. Current pipeline robots have drawbacks like limited diameter adaptability, high traveling resistance and poor navigation. Manual inspection is inefficient. This paper presents an adaptive spiral-loop pipeline robot using Laser slam. Its mechanical structure has a spiral mode and loop design to reduce resistance. It employs a multi-stage screw-nut-spring support rod type diameter-changing mechanism to adapt to pipes of different diameters. For localization in GPS-ineffective underground, Laser slam is applied. After validating the solution's feasibility via structure and trajectory analysis, the ROS/Gazebo and evo tool are used for simulation. Quantitative analysis shows the robot's average absolute error in the pipeline is under 0.58m and stability standard deviation is less than 0.27m, proving Laser slam's applicability. © 2024 IEEE.

关键词： slam robotics

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Technology of Mobile Robot Localization Assisted byQRCodes 4

Technology of Mobile Robot Localization Assisted byQRCodes

引用

4th International Conference on Industrial Automation, robotics and Control Engineering, IARCE 2024

作者： Wang, Yumeng Zhou, Qiang Li, Honglin Yue, Haosong School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350380323

With the improvement of productivity levels, people's demand for enhanced functionality and expanded application scenarios of mobile robots is increasing. Mobile robots are playing an increasingly critical role in several fields. Ensuring that mobile robots can achieve high-precision localization in various environments has become the basis for its role. However, traditional laser-based localization technologies suffer from significant errors in areas with indistinct environmental features. Firstly, the QR code is deeply studied, including its characteristics, classification and function principle in localization calculation. To address this, a method that utilizes QR codes for assisting in robot localization is proposed. To validate the proposed method, a specific QR code was selected and the key technologies for mobile robot localization were thoroughly studied. The method integrates QR code information using laser-based radar and the Cartographer slam algorithm to achieve accurate localization. To verify the research results, a Gazebo simulation platform for mobile robots was established, and the simulations and physical validations of the proposed method were completed. The results show that the QR code assisted localization technology can significantly improve the localization accuracy in the area with not obvious environmental characteristics. Moreover, through the research of mobile robot localization technologies, it has a significant promoting effect on the cultivation of students' engineering practice and innovation ability. © 2024 IEEE.

关键词： slam robotics

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Legolas: Deep Leg-Inertial Odometry 8

Legolas: Deep Leg-Inertial Odometry

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8th Conference on Robot Learning, CoRL 2024

作者： Wasserman, Justin Agarwal, Ananye Jangir, Rishabh Chowdhary, Girish Pathak, Deepak Gupta, Abhinav University of Illinois Urbana-Champaign United States Carnegie Mellon University United States

Estimating odometry, where an accumulating position and rotation is tracked, has critical applications in many areas of robotics as a form of state estimation such as in slam, navigation, and controls. During deployment of a legged robot, a vision system's tracking can easily get lost. Instead, using only the onboard leg and inertial sensor for odometry is a promising alternative. Previous methods in estimating leg-inertial odometry require analytical modeling or collecting high-quality real-world trajectories to train a model. Analytical modeling is specific to each robot, requires manual fine-tuning, and doesn't always capture real-world phenomena such as slippage. Previous work learning legged odometry still relies on collecting real-world data, this has been shown to not perform well out of distribution. In this work, we show that it is possible to estimate the odometry of a legged robot without any analytical modeling or real-world data collection. In this paper, we present Legolas, the first method that accurately estimates odometry in a purely data-driven fashion for quadruped robots. We deploy our method on two real-world quadruped robots in both indoor and outdoor environments. In the indoor scenes, our proposed method accomplishes a relative pose error that is 73% less than an analytical filtering-based approach and 87.5% less than a real-world behavioral cloning approach. More results are available at: ***. © 2024 Proceedings of Machine Learning Research.

关键词： slam robotics

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Enhanced GMapping Algorithm for Static Glass Detection

Enhanced GMapping Algorithm for Static Glass Detection

引用

2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Qi, Bowen Mo, Qian Zhu, Bo School of Information and Intelligence Engineering Guangzhou Xinhua University Guangzhou China Beijing Baidu Netcom Science Technology Co. Ltd. Beijing China Center for Advanced Control and Smart Operations Nanjing University Suzhou China

ISBN: (纸本)9798350384185

Detecting glass targets and obstacles has significant practical importance in modern society. However, the state-of-art GMapping algorithms suffer from inaccuracies in identifying and mapping their positions when used to deal with highly transparent and reflective glass obstacles under the conditions with only laser sensors. This finally results in incomplete maps and potential navigation errors in robot slam applications. To overcome the limitation, the paper presents an enhanced GMapping algorithm by incorporating a glass detection method to fuse laser and ultrasonic data. By exploiting the disparity in distances measured by these two sensors, the method effectively detects glass and refines the algorithm by substituting erroneous distance data with accurate ones. The indoor experimental results demonstrate the improvement of mapping accuracy and reduction of navigation errors owing to the application of the proposed sensor fusion method. © 2024 IEEE.

关键词： slam robotics

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A Review of the development of Visual slam 4

A Review of the development of Visual SLAM

引用

4th International Conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2024

作者： Dai, Zhengao Soochow University School of Future Science and Engineering Suzhou China

ISBN: (纸本)9798331531225

Simulated Localization and Mapping (slam) technology is one of the core technologies in the navigation field of autonomous mobile robots and unmanned aerial vehicles, which enables the machines to simultaneously locate themselves and build environmental maps through visual sensors (such as cameras). This technology has important applications in the fields of self-driving cars, augmented reality, and automatic navigation. This paper aims to provide a comprehensive overview of visual slam, focusing on its theoretical basis, key technologies, and development in the past decades. This paper first summarizes the basic principle and classification of slam technology, and then deeply discusses the theoretical basis of visual slam, including image processing, computer vision, state estimation, and kinematics. Then, the paper introduces the development of key technologies of visual slam in detail. Finally, this paper discusses the application of visual slam in UAV navigation, robot exploration, and virtual reality, and points out the limitations of current technology and the future research direction, including multi-sensor fusion, deep learning application, real-time performance improvement and slam technology in antagonistic environment. The main contribution of this review is to sort out the latest progress of visual slam in detail and look forward to the future development trend and potential research direction. © 2024 IEEE.

关键词： slam robotics

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Research on visual slam based on improved YOLOv5s in dynamic scenes 4

Research on visual slam based on improved YOLOv5s in dynamic...

引用

2024 4th International Conference on Mechatronics, Automation and Intelligent Control, MAIC 2024

作者： Shen, Dong Xu, Haobo Zhang, Boxuan Cao, Meng Ma, Liang School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China Ningxia Hui Autonomous Region Radio Monitoring Station Yinchuan750002 China

To address inaccuracies in camera position computations induced by dynamic objects in visual simultaneous localization and mapping (vslam), a method for dynamic feature point detection and rejection is given. This method merges an improved YOLOv5 lightweight network with the lucas-kanade (LK) optical flow. In the GhostNet network architecture, the typical convolutional layer is initially replaced by GhostConv separable convolution. At the end of the feature extraction network, a spatial attention mechanism based on CA is incorporated to safeguard crucial positional information and improve detection precision. This method efficiently eliminates dynamic elements, resulting in a point cloud map that is denser. Evaluations against the TUM dataset indicate that the proposed approach considerably surpasses previous slam methods regarding real-time efficiency and localization accuracy. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： slam robotics

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Camera / System Calibration of LiDAR-Vision Sensor Module Mounted on A Ground Disaster Investigation Robot 45

Camera / System Calibration of LiDAR-Vision Sensor Module Mo...

引用

45th Asian Conference on Remote Sensing, ACRS 2024

作者： Kim, S.S. Jung, Y.H. Lim, E.T. Koo, S. Disaster Scientific Investigation Div National Disaster Management Research Institute Korea Republic of

The camera and system calibration process is an essential pre-processing step for ensuring the accuracy of the LiDAR/Vision-based Simultaneous Localization and Mapping (slam) module. Firstly, camera calibration estimates the interior orientation parameters (IOPs). Additionally, system calibration calculates the relative positions and attitudes of individual sensors which constitute the multi-sensor module of the investigation robot, geometrically enhancing the overall observation accuracy of the system. Experiments for camera and system calibration were conducted by placing wall targets and V-shaped three-dimensional calibration boards in indoor spaces and acquiring images and point clouds for various experimental conditions. Considering the field of view between the camera and LiDAR, camera resolution, distance, and indoor illumination, the characteristics of noise generation in the LiDAR point cloud were confirmed. For system calibration of the multi-sensor module, images and point clouds for the V-shaped three-dimensional calibration board were obtained from 9 locations at a rate of 20 shots per second, while for camera calibration, image data for flat calibration boards on walls were obtained from 6 locations, considering distance and height variations. The results of camera and system calibration showed precise experimental outcomes, with the camera IOP estimation error (RMSE) through the calibration experiment being 0.39 pixels and the system calibration between LiDAR and camera showing errors within 0.2 pixels. Based on the experimentally derived system and camera calibration results, it is anticipated that the enhanced slam accuracy of future investigation robots in indoor spaces and more precise and accurate detection and analysis of indoor disaster information could be achieved. © 2024 45th Asian Conference on Remote Sensing, ACRS 2024. All rights reserved.

关键词： slam robotics

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Active Bionic Visual slam and Semantic Map Generation in Building Environments 4

Active Bionic Visual SLAM and Semantic Map Generation in Bui...

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4th International Conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Cao, Yifan Wang, Kaifang Song, Wei Shanghai University School of Mechatronic Engineering and Automation Shanghai China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai China

ISBN: (纸本)9798331534622

Simultaneous localization and mapping (slam) have extensive applications in the field of robotics. However, current visual slam mainly constructs semantic maps using passive image information and lacks the ability to autonomously explore the environment. To address this issue, this paper proposes an active semantic capture slam system based on a bionic stereo camera, designed for structured environments such as corridors. The system first uses a pose estimation network for initial target localization, then uses the six-degree-of-freedom bionic stereo camera to dynamically adjust the viewpoint, autonomously control the camera to fixate on the target, and optimize the target's pose. Additionally, it analyzes surrounding feature points to determine the position of textual information, guiding the camera to capture it. Experimental results show that, compared to the fixed ORB-slam2, the proposed system improves target localization accuracy by an average of 36.1%, effectively integrates object and textual semantic information, and enriches the semantic map. © 2024 IEEE.

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