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Electronic Sensor Multi-Modal slam algorithm Based on Information Fusion Technology 1

Electronic Sensor Multi-Modal Slam Algorithm Based on Inform...

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Wang, Chao Qiu, Yijian Wuhan University of Engineering Science Hubei Wuhan China Institute of Applied Physics Jiangxi Academy of Sciences Nanchang Jiangxi China

ISBN: (纸本)9798350316469

When the multi-mode electronic sensor based on information fusion works, it will be interfered by various noises in the process of data acquisition and transmission, and the signal will appear nonlinear distortion. Therefore, this paper needs to compensate the input analog quantity to a certain extent. In this paper, multiple sensors such as lidar and camera are used to obtain environmental information. These sensors measure the environment, resulting in a series of data points. These data points are input into the information fusion system for processing to obtain a high-precision environment model. The information fusion system uses the slam method to estimate and correct the measurement results of each sensor, so as to obtain a more accurate environmental model. This paper describes the sensor and the algorithm used in it, and obtained the data through case analysis and multi-modal experiments. The experimental results show that the error of the multi-modal slam algorithm is reduced to 1.1 %, and the accuracy is 98.9%. This has a great effect on the reconstruction variable and parameter extraction accuracy in the target environment. © 2023 IEEE.

关键词： Electronic Sensor Information Fusion Multimodal Analysis slam algorithm

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Application of Indoor Motion Measurement Technology based on slam algorithm in Underground Engineering 3

Application of Indoor Motion Measurement Technology based on...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Lu, Qiang Chong Qing Vocational Institute of Engineering Chong Qing4022260 China

ISBN: (纸本)9798350319125

slam technology is one of the important technologies in modern robot, automatic driving and other fields, which can carry out autonomous positioning and map building in unknown environment. In indoor motion measurement, slam algorithm also plays an important role, and its role and influence can be analyzed from the following aspects: Then, the visual sensor is used to measure the indoor movement of the underground project, form the indoor movement measurement scheme, and measure the indoor movement Conduct a comprehensive analysis. The formula estimation shows that under the condition of a certain measurement standard, the slam algorithm is a measurement technology for indoor movement of underground engineering The accuracy and the application time of indoor mobile measurement technology are better than the AMCL algorithm. © 2023 IEEE.

关键词： AMCL algorithm slam algorithm underground works vision sensor

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Visual Recognition and Localization of Industrial Robots Based on slam algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第11期15卷 1376-1380页

作者： Cui, Wei Zhao, Yuefan Sun, Litao Hebei Inst Mech & Elect Technol Dept Elect Engn Xingtai 054000 Hebei Peoples R China Hebei Inst Mech & Elect Technol Dept Mech Engn Xingtai 054000 Hebei Peoples R China

The front-end feature matching module of traditional slam systems is characterized by sparse or dense feature points, it is difficult to generate accurate camera trajectory and scene reconstruction results, in response to this problem, the author studied a fast reconstruction algorithm for any path based on V-slam, by using improved feature matching algorithms to accurately match feature points, the accuracy of scene sparse reconstruction and camera trajectory recovery has been improved, the backend optimization thread adopts segmented optimization matching to reduce the computational burden of reconstruction, and the performance of the V-slam system was improved through parallel processing, the matching results and camera trajectory error comparison results showed that the improved V-slam algorithm can quickly recover camera trajectory and scene reconstruction, with the development of multi-sensor collaborative coupling and multi view fusion technology, the V-slam method proposed by the author can add virtual 3D objects to real scenes, and the Vslam system can extract feature points in the screen in real-time and detect planar objects in the scene, ensure that multiple virtual objects in the scene meet geometric consistency with the actual scene, in the experiment, two objects were added to the virtual scene, users can interactively scale objects and add them without being affected by camera movements, ensuring consistency between objects and the real scene.

关键词： slam algorithm industrial robot visual recognition location

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Hardware Implementation of slam algorithm

Hardware Implementation of SLAM Algorithm

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作者： Radhanpuri, Shiban California State University Northridge

学位级别：博士

The purpose of the project is to design and test of a slam algorithm and then assembling the hardware's on a Pi- Car (prototype robot) which can move and travel through different place and generate a 2D map out of the places. With the current competition in the market as most of the fortune 500 companies are trying to use this technology (slam algorithm) on different platforms like autonomous Car (Google Car, Tesla Car) etc., we thought it was a good idea if we could built a prototype for the same. The slam algorithm implemented on the robot works on the principle of Localization, mapping and navigate itself in the room (for out project). The System uses mainly Raspberry-Pi model B which integrates with the Lidar and Ultrasonic sensor. The Lidar sensor does laser scanning and gives accurate result back to the raspberry-pi, likewise the ultrasonic sensor gives accurate data (for close range), which detects any obstacle in front of it and sends the signal to the raspberry-pi to make a turn for the Pi- Car.

关键词： VLSI DIGITAL RASPBERRY-PI LIDAR slam algorithm ULTRASONIC RVIZ PYTHON

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Overview of 2D Laser slam algorithm Research

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第9期8卷 397-402页

作者： Dongna Xie Feifan Zhao Guodong Xiu

slam, or simultaneous localization and mapping, is primarily used to solve the problem of mobile robot positioning, navigation, and mapping in unknown environments. Among these, 2D laser slam has low cost and high accuracy in building drawings in an indoor environment. This paper describes the fundamental framework of 2D laser slam. Several popular slam algorithms are discussed. Finally, future developments in laser slam are anticipated.

关键词： Inter-frame Matching Loopback Detection Back-end Optimization slam algorithm

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Design of Elderly Care System Integrated with slam algorithm 2nd

Design of Elderly Care System Integrated with SLAM Algorithm

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2nd International Conference on Security with Intelligent Computing and Big-data Services (SICBS)

作者： Chen, Jun-Yan Huang, Long Guilin Univ Elect Technol Guilin Peoples R China

ISBN: (纸本)9783030169466;9783030169459

Currently, China has entered the aging society, and the problems of elderly care have been focused by the whole society. In order to help the young people to take care of the elderly, a care service system integrated with slam algorithm was designed. This system includes four parts: ZigBee networking equipments, robot, cloud server and Andriod APP. It makes the robot based on slam algorithm achieve indoor navigation and map building. And it provides convenience for the disabled people, monitors the environment security information in real time and shows the physical data of the elderly, which can help young people who work outside master the physical healthy conditions of the elderly who are at home.

关键词： Elderly care slam algorithm Robot ZigBee

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An Optimized Coverage Robot slam algorithm Based on Improved Particle Filter for WSN Nodes

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INTERNATIONAL JOURNAL OF GRID AND HIGH PERFORMANCE COMPUTING 2020年第4期12卷 76-88页

作者： Zhang, Wei Du, Yanli Bai, Qinghua Beihua Univ Jilin Jilin Peoples R China

In order to realize the positioning and creation of the environment of mobile robots, this article proposes an optimized coverage robot slam algorithm based on an improved particle filter for WSN nodes. The algorithm overcomes the disadvantages of the standard particle filter slam algorithm in the simultaneous positioning of robot poses and creation of environmental maps. By constructing the sensor node to cover the high coverage of the slam positioning information node of the robot, the algorithm can search for the ideal result under the existing information, and the local optimization is performed to obtain the ideal result in another local state. Thus, the global accurate robot slam information is finally obtained. Simulation experiments show that the influence of the time delay parameter for simultaneous positioning of the robot slam is almost zero at different speeds, which shows the superior positioning stability of the new algorithm.

关键词： Delay Estimation Differential Evolution algorithm Particle Filter Robot slam algorithm

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Feature-Based slam algorithm for Small Scale UAV with Nadir View 20th

Feature-Based SLAM Algorithm for Small Scale UAV with Nadir ...

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20th International Conference on Image Analysis and Processing (ICIAP)

作者： Avola, Danilo Cinque, Luigi Fagioli, Alessio Foresti, Gian Luca Massaroni, Cristiano Pannone, Daniele Sapienza Univ Dept Comp Sci Via Salaria 113 I-00198 Rome Italy Univ Udine Dept Math Comp Sci & Phys Via Sci 206 I-33100 Udine Italy

ISBN: (纸本)9783030306458;9783030306441

Small-scale Unmanned Aerial Vehicles (UAVs) have recently been used in several application areas, including search and rescue operations, precision agriculture, and environmental monitoring. Telemetry data, acquired by GPSs, plays a key role in supporting activities in areas like those just reported. In particular, this data is often used for the real-time computation of UAVs paths and heights, which are basic prerequisites for many tasks. In some cases, however, the GPS sensors can lose their satellite connection, thus making the telemetry data acquisition impossible. This paper presents a feature-based Simultaneous Localisation and Mapping (slam) algorithm for small-scale UAVs with nadir view. The proposed algorithm allows to know the travelled route as well as the flight height by using both a calibration step and visual features extracted from the acquired images. Due to the novelty of the proposed algorithm no comparisons with other methods are reported. Anyway, extensive experiments on the recently released UAV Mosaicking and Change Detection (UMCD) dataset have shown the effectiveness and robustness of the proposed algorithm. The latter and the dataset can be used as baseline for future research in this application area.

关键词： UAVs slam algorithm Mosaicking A-KAZE

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Research on slam algorithm of Iterated Extended Kalman Filtering for Multi-sensor Fusion 17

Research on Slam Algorithm of Iterated Extended Kalman Filte...

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3rd International Conference on Communication and Information Processing (ICCIP)

作者： Shi Han-hai Dang Shu-wen He Fa-jiang Wang Kang-le Shanghai Univ Engn Sci Sch Air Transportat & Flying Shanghai 201620 Peoples R China

ISBN: (纸本)9781450353656

For the problem of the error of EKF in linearization, an iterated extended Kalman filtering algorithm based on IMU and laser sensor for environmental feature matching is proposed. The K-Nearest Neighbor algorithm is applied to match the data obtained by IMU and laser sensor. In the measurement phase, the nonlinear system is linearized multiple by EKF. The simulation results show that the new method effectively reduces the nonlinear error and inhibits the error accumulation caused by IMU. Compared with traditional extended Kalman filtering, the root mean square error of position and azimuth was reduced by 48%, 16% and 29%, respectively. As the result, the navigation accuracy of system is improved and state tracking performance outperforms better than traditional EKF.

关键词： Multi-sensor fusion iterated extend kalman filtering slam algorithm

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An Adaptive Square Root Cubature Kalman Filter based slam algorithm for Mobile Robots 12

An Adaptive Square Root Cubature Kalman Filter based SLAM Al...

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IEEE International Conference on Mechatronics & Automation

作者： Cai, Jun Zhong, Xiaolin Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China

ISBN: (纸本)9781479970988

For simultaneous localization and mapping (slam) of mobile robots, an innovative solution is proposed, named adaptive square root cubature Kalman filter based slam algorithm (ASRCKF-slam). The main contribution of the proposed algorithm lies that: 1) Square root factors are used in the proposed ASRCKF-slam algorithm to improve the calculation efficiency by avoiding the time-consuming Cholesky decompositions. 2) Using the adaptive Sage-Husa estimator to solve the large estimation errors or even divergence problem caused by the time-varying or unknown noise. Simulation results obtained demonstrate that the proposed ASRCKF-slam algorithm is superior to the existed slam method in the aspect of estimation accuracy and computational efficiency.

关键词： slam algorithm ASRCKF Sage-Husa estimator Adaptive Mobile robot

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