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Review of Simultaneous Localization and Mapping Technology in the Agricultural Environment
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作者 Yaoguang Wei Bingqian Zhou +3 位作者 Jialong Zhang Ling Sun Dong An Jincun Liu 《Journal of Beijing Institute of Technology》 EI CAS 2023年第3期257-274,共18页
Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve th... Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments and contribute to the realization of real-time obstacle avoidance and dynamic path planning.Moreover,the application of SLAM technology has expanded from industrial production,intelligent transportation,special operations and other fields to agricultural environments,such as autonomous navigation,independent weeding,three-dimen-sional(3D)mapping,and independent harvesting.This paper mainly introduces the principle,sys-tem framework,latest development and application of SLAM technology,especially in agricultural environments.Firstly,the system framework and theory of the SLAM algorithm are introduced,and the SLAM algorithm is described in detail according to different sensor types.Then,the devel-opment and application of SLAM in the agricultural environment are summarized from two aspects:environment map construction,and localization and navigation of agricultural robots.Finally,the challenges and future research directions of SLAM in the agricultural environment are discussed. 展开更多
关键词 simultaneous localization and mapping(SLAM) agricultural environment agricultural robots environment map construction localization and navigation
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Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time
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作者 Muhammad S.Alam Farhan B.Mohamed +2 位作者 Ali Selamat Faruk Ahmed AKM B.Hossain 《Intelligent Automation & Soft Computing》 2024年第3期417-436,共20页
Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance o... Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance. 展开更多
关键词 Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)
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Approach of simultaneous localization and mapping based on local maps for robot 被引量:6
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作者 陈白帆 蔡自兴 胡德文 《Journal of Central South University of Technology》 EI 2006年第6期713-716,共4页
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 ob... 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. 展开更多
关键词 simultaneous localization and mapping extended Kalman filter local map
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Research on simultaneous localization and mapping for AUV by an improved method:Variance reduction FastSLAM with simulated annealing 被引量:5
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作者 Jiashan Cui Dongzhu Feng +1 位作者 Yunhui Li Qichen Tian 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2020年第3期651-661,共11页
At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method o... At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system. 展开更多
关键词 Autonomous underwater vehicle(AUV) SONAR Simultaneous localization and mapping(SLAM) Simulated annealing FASTSLAM
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A novel method for mobile robot simultaneous localization and mapping 被引量:4
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作者 LI Mao-hai HONG Bing-rong +1 位作者 LUO Rong-hua WEI Zhen-hua 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第6期937-944,共8页
A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment.... A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable. 展开更多
关键词 Mobile robot Rao-Blackwellized particle filter (RBPF) Monocular vision Simultaneous localization and mapping (SLAM)
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Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping 被引量:4
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作者 李枚毅 蔡自兴 《Journal of Central South University of Technology》 EI 2006年第5期529-535,共7页
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 de... 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. 展开更多
关键词 immune evolutionary algorithms simultaneous localization and mapping domain knowledge
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A survey: which features are required for dynamic visual simultaneous localization and mapping? 被引量:2
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作者 Zewen Xu Zheng Rong Yihong Wu 《Visual Computing for Industry,Biomedicine,and Art》 EI 2021年第1期183-198,共16页
In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the po... In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article. 展开更多
关键词 Dynamic simultaneous localization and mapping Multiple objects tracking Data association Object simultaneous localization and mapping Feature choices
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Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping 被引量:2
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作者 SONG Yu SONG Yongduan LI Qingling 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第4期693-700,共8页
Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major d... Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM (ISP-FastSLAM) algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter (ISPKF), which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts: in the first part, an iterated sigma point particle filter (ISPPF) to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm. 展开更多
关键词 mobile robot simultaneous localization and mapping (SLAM) particle filter Kalman filter unscented transformation
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Localization and mapping in urban area based on 3D point cloud of autonomous vehicles 被引量:2
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作者 王美玲 李玉 +2 位作者 杨毅 朱昊 刘彤 《Journal of Beijing Institute of Technology》 EI CAS 2016年第4期473-482,共10页
In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping (SLAM) algorithm, which uses a VoxelGrid filter to down sample the point cloud data, ... In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping (SLAM) algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points (ICP) algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles' importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles' poses, which would decrease particle numbers, increase algorithm speed and restrain particles' impoverishment. The calculation of particles' importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches. 展开更多
关键词 simultaneous localization and mapping (SLAM) Rao-Blackwellized particle filter RB-PF) VoxelGrid filter ICP algorithm Gaussian model urban area
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Rapid State Augmentation for Compressed EKF-Based Simultaneous Localization and Mapping 被引量:1
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作者 窦丽华 张海强 +1 位作者 陈杰 方浩 《Journal of Beijing Institute of Technology》 EI CAS 2009年第2期192-197,共6页
A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping (CEKF-SLAM) algorithm was proposed. State augment usually requi... A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping (CEKF-SLAM) algorithm was proposed. State augment usually requires a fully-updated state eovariance so as to append the information of newly observed landmarks, thus computational volume increases quadratically with the number of landmarks in the whole map. It was proved that state augment can also be achieved by augmenting just one auxiliary coefficient ma- trix. This method can yield identical estimation results as those using EKF-SLAM algorithm, and computa- tional amount grows only linearly with number of increased landmarks in the local map. The efficiency of this quick state augment for CEKF-SLAM algorithm has been validated by a sophisticated simulation project. 展开更多
关键词 simultaneous localization and mapping (SLAM) extended Kalman filter state augment compu- tational volume
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Simultaneous Localization and Mapping System Based on Labels 被引量:1
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作者 Tong Liu Panpan Liu +1 位作者 Songtian Shang Yi Yang 《Journal of Beijing Institute of Technology》 EI CAS 2017年第4期534-541,共8页
In this paper a label-based simultaneous localization and mapping( SLAM) system is proposed to provide localization to indoor autonomous robots. In the system quick response( QR) codes encoded with serial numbers ... In this paper a label-based simultaneous localization and mapping( SLAM) system is proposed to provide localization to indoor autonomous robots. In the system quick response( QR) codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams,then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter( EKF) for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes,such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision. 展开更多
关键词 simultaneous localization and mapping (SLAM) extended Kalman filter (EKF) quick response (QR) codes artificial landmarks
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Underwater Simultaneous Localization and Mapping Based on Forward-looking Sonar 被引量:1
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作者 Tiedong Zhang Wenjing Zeng Lei Wan 《Journal of Marine Science and Application》 2011年第3期371-376,共6页
A method of underwater simultaneous localization and mapping (SLAM) based on forward-looking sonar was proposed in this paper. Positions of objects were obtained by the forward-looking sonar, and an improved associa... A method of underwater simultaneous localization and mapping (SLAM) based on forward-looking sonar was proposed in this paper. Positions of objects were obtained by the forward-looking sonar, and an improved association method based on an ant colony algorithm was introduced to estimate the positions. In order to improve the precision of the positions, the extended Kalman filter (EKF) was adopted. The presented algorithm was tested in a tank, and the maximum estimation error of SLAM gained was 0.25 m. The tests verify that this method can maintain better association efficiency and reduce navigatioJ~ error. 展开更多
关键词 simultaneous localization and mapping (SLAM) looking forward sonar extended Kalman filter (EKF)
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FIXED POINT THEOREMS OF LOCAL CONTRACTION MAPPINGS ON MENGER SPACES
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作者 方锦暄 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 1991年第4期363-372,共10页
In this paper,we introduce the concept ofε-chainable PM-space,and give severalfixed point theorems of one-valued and multivalued local contraction mapping on the kindof spaces.
关键词 probabilistic metric space Menger space e-chainable local contraction mapping fixed point
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Design of Lattice Structures Using Local Relative Density Mapping Method
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作者 Guo-Hua Song Shi-Kai Jing +3 位作者 Fang-Lei Zhao Ye-Dong Wang Hao Xing Long-Fei Qie 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2018年第5期110-118,共9页
In order to solve the problem of substantial computational resources of lattice structure during optimization, a local relative density mapping(LRDM) method is proposed. The proposed method uses solid isotropic micros... In order to solve the problem of substantial computational resources of lattice structure during optimization, a local relative density mapping(LRDM) method is proposed. The proposed method uses solid isotropic microstructures with penalization to optimize a model at the macroscopic scale. The local relative density information is obtained from the topology optimization result. The contour lines of an optimized model are extracted using a density contour approach, and the triangular mesh is generated using a mesh generator. A local mapping relationship between the elements’ relative density and the struts’ relative cross?sectional area is established to automatically determine the diameter of each individual strut in the lattice structures. The proposed LRDM method can be applied to local finite element meshes and local density elements, but it is also suitable for global ones. In addition, some cases are con?sidered in order to test the e ectiveness of the LRDM method. The results show that the solution time of the LRDM is lower than the RDM method by approximately 50%. The proposed method provides instructions for the design of more complex lattice structures. 展开更多
关键词 Lattice structures local relative density mapping Topology optimization Additive manufacturing
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Mobile robot localization algorithm based on multi-sensor information fusion 被引量:10
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作者 WANG Ming-yi HE Li-le +1 位作者 LI Yu SUO Chao 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2020年第2期152-160,共9页
In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm ba... In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm based on multi-sensor information fusion(MSIF)was proposed.In this paper,simultaneous localization and mapping(SLAM)was realized on the basis of laser Rao-Blackwellized particle filter(RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo localization.The feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF(ORB)were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional(3D)point feature in binocular visual reconstruction environment.Factor graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual localization.The results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms,and the effectiveness and usefulness of the proposed method are verified. 展开更多
关键词 mobile robot simultaneous localization and mapping(SLAM) graph-based optimization sensor fusion
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Dense Mapping From an Accurate Tracking SLAM 被引量:4
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作者 Weijie Huang Guoshan Zhang Xiaowei Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第6期1565-1574,共10页
In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requi... In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time. 展开更多
关键词 Adaptive weights data association dense mapping hash table simultaneous localization and mapping(SLAM)
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Robust SLAM localization method based on improved variational Bayesian filtering 被引量:1
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作者 Zhai Hongqi Wang Lihui +1 位作者 Cai Tijing Meng Qian 《Journal of Southeast University(English Edition)》 EI CAS 2022年第4期340-349,共10页
Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise,outli... Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise,outliers,or unknown and time-varying noise statistical characteristics,a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed.First,the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm.Then,the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average.Finally,the system updates are iterated multiple times to further gradually correct the state estimation error.Furthermore,to observe features at different depths,a feature measurement model containing depth parameters is constructed.Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information,compared with the variational Bayesian adaptive SLAM method,the positioning accuracy of the proposed method is improved by 17.23%,20.46%,and 17.76%,which has better applicability and robustness to environmental disturbance. 展开更多
关键词 underwater navigation and positioning non-Gaussian distribution time-varying noise variational Bayesian method simultaneous localization and mapping(SLAM)
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Precise Transceiver-Free Localization in Complex Indoor Environment 被引量:3
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作者 Rui Mao Peng Xiang Dian Zhang 《China Communications》 SCIE CSCD 2016年第5期28-37,共10页
Transceiver-free object localization can localize target through using Radio Frequency(RF) technologies without carrying any device, which attracts many researchers' attentions. Most traditional technologies usual... Transceiver-free object localization can localize target through using Radio Frequency(RF) technologies without carrying any device, which attracts many researchers' attentions. Most traditional technologies usually first deploy a number of reference nodes which are able to communicate with each other, then select only some wireless links, whose signals are affected the most by the transceiver-free target, to estimate the target position. However, such traditional technologies adopt an ideal model for the target, the other link information and environment interference behavior are not considered comprehensively. In order to overcome this drawback, we propose a method which is able to precisely estimate the transceiver-free target position. It not only can leverage more link information, but also take environmental interference into account. Two algorithms are proposed in our system, one is Best K-Nearest Neighbor(KNN) algorithm, the other is Support Vector Regression(SVR) algorithm. Our experiments are based on Telos B sensor nodes and performed in different complex lab areas which have many different furniture and equipment. The experiment results show that the average localization error is round 1.1m. Compared with traditional methods, the localization accuracy is increased nearly two times. 展开更多
关键词 indoor localization transceiver-free radio map support vector regression
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Mobile robot simultaneous localization and map building based on improved particle filter
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作者 厉茂海 Hong Bingrong Wei Zhenhua 《High Technology Letters》 EI CAS 2006年第4期385-391,共7页
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 poste... 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. 展开更多
关键词 mobile robot particle filter simultaneous localization and mapping Hough transform extended Kalman filter
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Multi-robot mapping based on the adaptive differential evolution
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作者 刘利枚 CaiZixing 《High Technology Letters》 EI CAS 2013年第1期7-11,共5页
Map building by multi-robot is very important to accomplish autonomous navigation,and one of the basic problems and research hotspots is how to merge the maps into a single one in the field of multi-robot map building... Map building by multi-robot is very important to accomplish autonomous navigation,and one of the basic problems and research hotspots is how to merge the maps into a single one in the field of multi-robot map building.A novel approach is put forward based on adaptive differential evolution to map building for the multi-robot system.The multi-robot mapping-building system adopts the methods of decentralized exploration and concentrated mapping.The adaptive differential evolution algorithm is used to search in the space of possible transformation,and the iterative search is performed with the goal of maximizing overlapping regions.The map is translated and rotated so that the two maps can be overlapped and merged into a single global one successfully.This approach for map building can be realized without any knowledge of their relative positions.Experimental results show that the approach is effective and feasibile. 展开更多
关键词 differential evolution algorithm cooperative simultaneous localization and mapping map building MULTI-ROBOT grid maps ADAPTIVE
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