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Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping

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Journal of Central South University of Technology 2006年第5期13卷 529-535页

作者：李枚毅蔡自兴 School of Information Science and Engineering Central South University

Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.

关键词： immune evolutionary algorithms simultaneous localization and mapping domain knowledge

Approach of simultaneous localization and mapping based on local maps for robot

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Journal of Central South University of Technology 2006年第6期13卷 713-716页

作者：陈白帆蔡自兴胡德文 School of Information Science and Engineering Central South University College of Mechatronics and Automation National University of Defense Technology

An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.

关键词： simultaneous localization and mapping extended Kalman filter local map

Research on feature extraction method based on simultaneous localization and mapping

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Research on feature extraction method based on Simultaneous ...

Practical Feature-Based simultaneous localization and mapping Using Sonar Data

第37届中国控制会议

作者： Dandan Wang Kaituo Tan Hongjie Li College of Electronic Information and Electrical Engineering Anyang Institute Of Technology

In view of the problems of low precision of localization and detection method based on traditional navigation with non-Gauss and non-linear model in underwater, a feature map generation algorithm based on the simultaneous localization and mapping（SLAM） was proposed. Image processing and feature extraction method based on SLAM were presented in this paper, and characteristic of shipwreck were matched and extracted respectively. The experimental results proved that the new algorithm could achieve higher correlation degree of feature matching and complete the feature map reconstruction. Finally, by building the system state model and observation model based on SLAM, and using Square-Root Cubature Kalman filter based on Spherical Simplex（SS-SRCKF） algorithm to estimate and analysis the state of carrier and the characteristics, the experimental results showed that the new filtering algorithm could realize high precision navigation with small error.

关键词： simultaneous localization and mapping image processing feature extraction Cubature Kalman filter

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Practical Feature-Based Simultaneous Localization and Mappin...

LiDAR-Visual SLAM with Integrated Semantic and Texture Information for Enhanced Ecological Monitoring Vehicle localization

第二十七届中国控制会议

作者： He Feng,Fang Yongchun,Wang Yutao,Ban Tao Institute of Robotics and Automatic Information System,Nankai University,Tianjin 300071,P.R.China

A practical feature-based SLAM is proposed for applications in indoor environment using sonar *** features are extracted from groups of sonar data using hybrid Hough Transform and Least Square（HTLS） *** meet the realtime requirement,an efficient implementation of HTLS algorithm based on slide window is ***,the standard formulation of EKF-SLAM is extended to 2D-line case where the practical plant and observation model is constructed and then *** illustrate the performance of the proposed algorithm,experiment results on a Pioneer mobile robot are provided,which demonstrate that the constructed SLAM technique exhibits superior practicality and simplicity.

关键词： simultaneous localization and mapping Sonar Extended Kalman filter Feature detection

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Computers, Materials & Continua 2025年第1期82卷 1401-1416页

作者： Yiqing Lu Liutao Zhao Qiankun Zhao Foreign Environmental Cooperation Center Ministry of Ecology and Environment of PRCBeijing100035China Beijing Computing Center Co. Ltd.Beijing Academy of Science and TechnologyBeijing100083China Beijing Guyu Interactive Artificial Intelligence Application Co. Ltd.Beijing100024China

Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental *** vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual simultaneous localization and mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant *** address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data ***,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less ***,visual texture information complements LiDAR-Visual localization by providing additional contextual *** incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of *** approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.

关键词： LiDAR-Visual simultaneous localization and mapping integrated semantic texture information

Mobile robot simultaneous localization and map building based on improved particle filter

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High Technology Letters 2006年第4期12卷 385-391页

作者：厉茂海 Hong Bingrong Wei Zhenhua Department of Computer Science and Technology Harbin Institute of Technology Harbin 150001 P.R. China

We present an investigation into the use of pan tilt zoom camera and sonar sensors for simuhaneous localization and mapping with artificial colored landmarks. An improved particle filter is applied to estimate a posterior of the pose of the robot, in which each particle has associated it with an entire map. The distributions of landmarks are also represented by particle sets, where separate particles are used to represent the robot and the landmarks. Hough transform is used to extract line segments from sonar observations and build map simultaneously. The key advantage of our method is that the full posterior over robot poses and landmarks can be nonlinearly approximated at every point in time by particles. Especially the landmarks are affixed on the moving robots, which can reduce the impact of the depletion problem and the impoverishment problem produced by basic particle filter. Experimental results show that this approach has advantages over the basic particle filter and the extended Kalman filter.

关键词： mobile robot particle filter simultaneous localization and mapping Hough transform extended Kalman filter

A Robust 2D Lidar SLAM Method in Complex Environment

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Photonic Sensors 2022年第4期12卷 51-65页

作者： Shan HUANG Hong-Zhong HUANG Qi ZENG Peng HUANG Center for System Reliability and Safety University of Electronic Science and Technology of ChinaChengdu 611731China EvenTec Co. Ltd.Chengdu 610097China Jiangxi University of Science and Technology Ganzhou 341000China

The two-dimensional(2D)lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the *** propose a robust 2D lidar simultaneous localization and mapping(SLAM)algorithm working in ambiguous *** improve the front-end scan-matching module’s accuracy and robustness,we propose performing degeneration analysis,line landmark tracking,and environment coverage *** max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous ***,we propose a tightly coupled framework that integrates lidar,wheel odometer,and inertial measurement unit(IMU).The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM *** experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8%of the existing *** it can run stably in ambiguous environments where the existing method will be ***,it ccan successfully construct a map with an area of more than 250000 square meters.

关键词： Robotics simultaneous localization and mapping 2D lidar multi-sensor fusion

An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1389-1400页

作者： He Zhang Lingqiu Jin Cang Ye The authors are with the Department of Computer Science Virginia Commonwealth UniversityRichmondVA 23284 USA

There are about 253 million people with visual impairment *** of them use a white cane and/or a guide dog as the mobility tool for daily *** decades of efforts,electronic navigation aid that can replace white cane is still research in *** this paper,we propose an RGB-D camera based visual positioning system(VPS)for real-time localization of a robotic navigation aid(RNA)in an architectural floor plan for assistive *** core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry(DVIO)method and a particle filter localization(PFL)*** estimates RNA’s pose by using the data from an RGB-D camera and an inertial measurement unit(IMU).It extracts the floor plane from the camera’s depth data and tightly couples the floor plane,the visual features(with and without depth data),and the IMU’s inertial data in a graph optimization framework to estimate the device’s 6-DOF *** to the use of the floor plane and depth data from the RGB-D camera,DVIO has a better pose estimation accuracy than the conventional VIO *** reduce the accumulated pose error of DVIO for navigation in a large indoor space,we developed the PFL method to locate RNA in the floor *** leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated *** on VPS,an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor *** results demonstrate that:1)DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation(18 Hz pose update rate)on a UP Board computer;2)PFL reduces the DVIO-accrued pose error by 82.5%on average and allows for accurate wayfinding(endpoint position error≤45 cm)in large indoor spaces.

关键词： Assistive navigation pose estimation robotic navigation aid(RNA) simultaneous localization and mapping visual-inertial odometry visual positioning system(VPS)

Visual attention and clustering-based automatic selection of landmarks using single camera

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Journal of Central South University 2014年第9期21卷 3525-3533页

作者： CHUHO Yi YONGMIN Shin JUNGWON Cho Future IT R&D Lab LG Electronics Home Appliance R&D Lab LG ElectronicsSeoul 153721 Korea Department of Computer Education Jeju National University Department of Mathematics and Computer Science Salisbury University

An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.

关键词： simultaneous localization and mapping automatic landmark selection visual attention clustering