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Research on Two-Round Self-Balancing Robot SLAM Based on the gmapping algorithm

SENSORS 2023年第5期23卷 2489页

作者： Zhao, Jianwei Li, Jinyu Zhou, Jiaxin China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

Aiming at the inconvenience of inspection and monitoring of coal mine pump room equipment in a narrow and complex environment, this paper proposes and designs a two-wheel self-balancing inspection robot based on laser SLAM. Using SolidWorks, the three-dimensional mechanical structure of the robot is designed, and the overall structure of the robot is analyzed by finite element statics. The kinematics model of the two-wheel self-balancing robot was established, and the multi-closed-loop PID controller was used to design the two-wheel self-balancing control algorithm of the robot. The 2D LiDAR-based gmapping algorithm was used to locate the robot and construct the map. Through the self-balancing test and anti-jamming test, it is verified that the self-balancing algorithm designed in this paper has a certain anti-jamming ability and good robustness. By using Gazebo to build a simulation comparison experiment, it is verified that the selection of the particle number is of great significance for improving the map accuracy. The actual test results show that the constructed map has high accuracy.

关键词： SLAM two-wheel self-balancing robot PID controller gmapping algorithm

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Optimization Design and Experimental Study of gmapping algorithm 32

Optimization Design and Experimental Study of Gmapping Algor...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Su, Zhifeng Zhou, Jiehua Dai, Jiyang Zhu, Yongguo Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Jiangxi Peoples R China Nanchang Hangkong Univ Sch Aeronaut Mfg Engn Nanchang 330063 Jiangxi Peoples R China

ISBN: (纸本)9781728158556

This paper aims at the issue of simultaneous localization and mapping of indoor mobile robot. In order to solve the defect of large computation and poor real-time performance of traditional particle filter algorithm, the gmapping algorithm based on Rao-Blackwellized particle filter introduces the improved proposal distribution with high-precision lidar data and adaptive resampling measure to construct two dimensional grid map. With the help of the robot operating system, the map construction of gmapping algorithm under different parameters is realized in two dimensional robot simulator. The performance of optimized gmapping algorithm is verified on the platform of differential mobile robot. Finally, the fusion of multi-sensor information and optimization design of gmapping algorithm are realized by calling the /imu/calibrate_imu service, updated and calibrated inertial measurement units data, and extended Kalman filter package. The simulation and experimental results show that the robot system can construct high precision environment map under the optimal parameter configuration.

关键词： Simultaneous Localization and Mapping gmapping algorithm Optimization Design Robot Operating System

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Optimization Design and Experimental Study of gmapping algorithm

Optimization Design and Experimental Study of Gmapping Algor...

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第32届中国控制与决策会议

作者： Zhifeng Su Jiehua Zhou Jiyang Dai Yongguo Zhu School of Information Engineering Nanchang Hangkong University School of Aeronautical Manufacturing Engineering Nanchang Hangkong University

This paper aims at the issue of simultaneous localization and mapping of indoor mobile *** order to solve the defect of large computation and poor real-time performance of traditional particle filter algorithm,the gmapping algorithm based on Rao-Blackwellized particle filter introduces the improved proposal distribution with high-precision lidar data and adaptive resampling measure to construct two dimensional grid *** the help of the robot operating system,the map construction of gmapping algorithm under different parameters is realized in two dimensional robot *** performance of optimized gmapping algorithm is verified on the platform of differential mobile ***,the fusion of multi-sensor information and optimization design of gmapping algorithm are realized by calling the/imu/calibratemu service,updated and calibrated inertial measurement units data,and extended Kalman filter *** simulation and experimental results show that the robot system can construct high precision environment map under the optimal parameter configuration.

关键词： Simultaneous Localization and Mapping gmapping algorithm Optimization Design Robot Operating System

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Research on Multi-Sensor Fusion SLAM algorithm Based on Improved gmapping

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IEEE ACCESS 2023年 11卷 13690-13703页

作者： Tian, Chengjun Liu, Haobo Liu, Zhe Li, Hongyang Wang, Yuyu Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130022 Peoples R China

Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy

关键词： Simultaneous localization and mapping Robots Laser radar Laser fusion Detection algorithms Measurement by laser beam Degradation Laser SLAM gmapping algorithm multi-sensor fusion SLAM system loop closure detection

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Indoor Robot Mapping and Navigation System Based on Cyber-Physical Systems: Integration of SLAM algorithm and Visual Information

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Zhao, Jianwei Wu, Yubo Bao, Wei Shi, Jingxiang Liu, Xingguo China Univ Min & Technol Beijing Fac Mech & Elect Engn Beijing 100083 Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing 100081 Peoples R China Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08854 USA Natl Open Univ Engn Res Ctr Integrat & Applicat Digital Learning Minist Educ Beijing 100039 Peoples R China

Synchronous Localization and Mapping (SLAM) and autonomous navigation technology are fundamental to the intelligence and automation of robots and they represent important research directions in intelligent transportation. This paper proposes an enhanced mapping algorithm based on Cyber-Physical Systems (CPS) to address the high precision demands of indoor robot autonomous navigation. The proposed system integrates a Kalman filter to fuse output data from multiple sensors, including a laser odometer, Inertial Measurement Unit (IMU), and wheel odometer, achieving precise multi-sensor fusion for localization. Additionally, the system combines the A* algorithm with the pure pursuit autonomous navigation algorithm to optimize autonomous navigation performance. Visual information processing incorporates Yolov5 for data augmentation and scene recognition, thereby improving the system's environmental perception. Experimental validation demonstrates an average mapping accuracy of 0.032 meters and an average navigation accuracy of 0.05 meters. The CPS architecture is highly effective for high-precision mapping and autonomous navigation in unknown and complex indoor environments, demonstrating the ability to improve localization and mapping accuracy in complex indoor environments.

关键词： Robots Mobile robots Wheels Autonomous robots Robot kinematics Odometry Odometers Location awareness Laser radar Simultaneous localization and mapping Autonomous navigation cyber-physical systems data enhancement gmapping algorithm indoor mobile robot Yolov 5

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Simulation of Map Construction of a Quadruped Robot in a Neutral Beam Injection System 3

Simulation of Map Construction of a Quadruped Robot in a Neu...

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3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)

作者： Wang, Ce Wu, Bin Liang, Lizhen Anhui Univ Hefei 230001 Peoples R China Chinese Acad Sci Inst Plasma Phys Hefei 230001 Peoples R China

ISBN: (纸本)9798350352634;9798350352627

The new quadruped robot platform can be used as a carrier for task robots in site inspection, data collection, and rescue and search. Positioning and mapping set up the premises for the robot to walk and inspect independently in the complex terrain environment. Within the CRAFT neutral beam injection system, the quadruped robot can be equipped with radiation measurement equipment to collect radiation data in the experimental hall. In the present work, the algorithm for environmental map construction at the existing site is validated by simulating the environmental map construction of the proposed quadruped robot platform, providing a theoretical basis for data acquisition and autonomous monitoring of the robot.

关键词： Quadruped robot ROS SLAM gmapping algorithm

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Research on real-time positioning and map construction technology of intelligent car based on ROS 19

Research on real-time positioning and map construction techn...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Liu, Rut Guan, Zhiwei li, Bin Wen, Guoqiang Liu, Bin Tianjin Univ Technol Tianin Key Lab Pr Adv Lfechatron Syst Design & In Sch Mech Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Ed Tianjin Peoples R China Tianjin Sino German Univ Applied Sci Tianjin 300350 Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

Real-time localization and map construction (SLAM) is a key technology to realize autonomous navigation of smart cars, which mainly solves the problems of mobile robots in mapping, positioning and path planning. This paper introduces and analyzes SLAM. By comparing three different mapping algorithms, gmapping, hector, and cartographer, and through analysis and comparison experiments, the gmapping-based algorithm is finally used for mapping. On the basis of AMCL positioning, global and local path planning is carried out through A* algorithm and DWA algorithm to realize autonomous navigation and obstacle avoidance functions. And the experimental verification was carried out under the autolabor smart car. The experiment proved that using this algorithm, autolabor can perform accurate pose estimation, map construction and autonomous navigation in an unfamiliar environment.

关键词： ROS Autonomous navigation Avoidance gmapping algorithm SLAM

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Research on SLAM algorithm and Navigation of Mobile Robot Based on ROS 18

Research on SLAM Algorithm and Navigation of Mobile Robot Ba...

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IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Liu, Bin Guan, Zhiwei Li, Bin Wen, Guoqiang Zhao, Yu Tianjin Univ Technol Sch Mech Engn Tianjin 300384 Peoples R China Tianjin Sino German Univ Appl Sci Tianjin 300350 Peoples R China

ISBN: (纸本)9781665441018

As an important research direction of mobile robots, SLAM is the core technology to realize intelligent autonomous mobile robots. For positioning, the robot needs a consistent map, for obtaining a map, the robot makes a good estimate of its position. This interdependence between positioning and mapping makes the SLAM problem difficult and necessary. This article first summarizes the representation method of the environment map, uses the ROS mobile robot to map the indoor environment, compares the mapping effect of the gmapping algorithm and the Cartographer algorithm, analyzes the results of the mapping, and proposes the optimal mapping plan. Aiming at the problem of simultaneous localization and mapping of robots, a ROS-based solution is proposed. Compare Dijkstra, A* algorithm and Dynamic Window Approach, and choose the best navigation algorithm.

关键词： ROS SLAM mapping gmapping algorithm Cartographer algorithm Path planning

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