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Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems最新文献

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On robot navigation using identical landmarks: integrating measurements from a time-of-flight laser 使用相同地标的机器人导航:从飞行时间激光集成测量
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398477
U. Larsson, Johan Forsberg, Å. Wernersson
{"title":"On robot navigation using identical landmarks: integrating measurements from a time-of-flight laser","authors":"U. Larsson, Johan Forsberg, Å. Wernersson","doi":"10.1109/MFI.1994.398477","DOIUrl":"https://doi.org/10.1109/MFI.1994.398477","url":null,"abstract":"This paper presents an algorithm for fusing scans from a time-of-flight laser and odometer readings from the robot. The range weighted Hough transform is used as a robust method to extract lines from the range data. The resulting peaks are used as feature coordinates when these lines/walls are used as landmarks during navigation. The associations between observations over the time sequence are made in a systematic way using a decision directed classifier. Natural geometrical landmarks are described in the robot frame together with a covariance matrix representing the spatial uncertainty. The map is thus built incrementally as the robot moves. If the map is given in advance the robot can find its location and navigate relative to the map. Experimental results and simulations are presented for a mobile robot with a scanning range measuring laser with 2 cm resolution.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126305222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Position estimation for mobile robot using sensor fusion 基于传感器融合的移动机器人位置估计
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398393
Daehee IiAiYGt, Ren C. Luo, Hideki Hashimoto, Fiiinio
{"title":"Position estimation for mobile robot using sensor fusion","authors":"Daehee IiAiYGt, Ren C. Luo, Hideki Hashimoto, Fiiinio","doi":"10.1109/MFI.1994.398393","DOIUrl":"https://doi.org/10.1109/MFI.1994.398393","url":null,"abstract":"An accurate position estimation is essential for a mobile robot, especially under partially known environment. Dead reckoning has been commonly used for position estimation. However this method has inherent problems because it also accumulate estimation errors. In this paper we propose two methods to increase the accuracy of estimated positions using multiple sensors information. One method is a probabilistic approach using Bayes rule, and the other is a matching method applying least squared scheme. Both of these two approaches use features, such as corner points and edges of the object in the task environment instead of land-marks. It is shown that we will be able to estimate the position of mobile robot precisely, in which errors are not cumulated.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123216385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Navigation of a quadruped robot in uneven terrain with multiple foot sensors 基于多足部传感器的四足机器人在不平坦地形中的导航
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398458
T. Oomichi, Y. Fuke, T. Hayashi
{"title":"Navigation of a quadruped robot in uneven terrain with multiple foot sensors","authors":"T. Oomichi, Y. Fuke, T. Hayashi","doi":"10.1109/MFI.1994.398458","DOIUrl":"https://doi.org/10.1109/MFI.1994.398458","url":null,"abstract":"In order to fully utilize legged mobile robots in practical applications, a variety of technologies must be significantly advanced including: motion control, leg motion planning, path planning from start to goal, and active foot placement sensing and selection. In an effort to further research in these areas, we have addressed the following issues: hierarchical robot control, map-based path generation that considers the robot's mobility capabilities, development of multi-mode sensing for foot placement planning, and foot placement control that succeeds with minimal apriori terrain information.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"47 2-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123456372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Multi-source information integration in intelligent systems using the plausibility measure 基于可信性测度的智能系统多源信息集成
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398426
Zhimeng Luo, Dehua Li
{"title":"Multi-source information integration in intelligent systems using the plausibility measure","authors":"Zhimeng Luo, Dehua Li","doi":"10.1109/MFI.1994.398426","DOIUrl":"https://doi.org/10.1109/MFI.1994.398426","url":null,"abstract":"Dempster-Shafer theory of evidence is particularly well suited for the aggregation and integration of information, however, a major disadvantage of this theory is that its time complexity increases geometrically as the number of evidential sources increases, In the paper, we develop a new multisource information fusion scheme using the plausibility measure. The method avoids using Dempster's rule of combination, in order to overcome the intractability of Dempster-Shafer computations, allowing the theory to be feasible in many more applications. A simple robotic vision system with object recognition data from multisensor is presented to highlight benefits of the new method.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124681543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Integrating multiple knowledge sources using genetic algorithm applied to hierarchically structured sensor data 将遗传算法应用于分层结构的传感器数据集成多个知识来源
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398427
T. Sawaragi, J. Umemura, O. Katai, S. Iwai
{"title":"Integrating multiple knowledge sources using genetic algorithm applied to hierarchically structured sensor data","authors":"T. Sawaragi, J. Umemura, O. Katai, S. Iwai","doi":"10.1109/MFI.1994.398427","DOIUrl":"https://doi.org/10.1109/MFI.1994.398427","url":null,"abstract":"The paper presents a new approach for implementing a human expert's proficient interpretation skills for data and knowledge fusion in signal understanding tasks. The authors start by recognizing the fact that signal interpretation is attributed much to a human expert's domain-specific, pattern-perceiving capability of grasping raw signals by structured representations having multiple levels of abstraction. First, they attempt to organize such structured representations by using the hierarchical clustering method of data analysis. Then, based on these representations they formulate a human expert's interpretation skills as an activity of searching for an optimum combination of those perceptual units within that structured representation space being constrained by the situational data. In order to implement this activity, they introduce a genetic algorithm and apply it to the structured representation space assimilating a human analyst's creative interpreting task of flexibly shifting the focal view of attention from the coarse to the precise. They implement a working system for signal understanding of the remote sensing data of seismic prospecting.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128033486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Wavelet-based primal vision model with selective attention mechanism 具有选择性注意机制的小波原始视觉模型
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398462
Aiqun Wang, Nan-Ning Zheng
{"title":"Wavelet-based primal vision model with selective attention mechanism","authors":"Aiqun Wang, Nan-Ning Zheng","doi":"10.1109/MFI.1994.398462","DOIUrl":"https://doi.org/10.1109/MFI.1994.398462","url":null,"abstract":"We propose a scheme for primal vision model. In this scheme, a new spatial-dependent sampling approach based on wavelets' characteristics is proposed to represent an image with partial information and to form multi-frequency channels. A feedback channel imitating the human's \"eye movement\" is introduced to form a close-loop control. Finally, we attempt to realize the primal vision model with wavelet-based selective attention processing mechanism. Some experiments show that the proposed scheme is reasonable and effective.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128326453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An active sensing method using estimated errors for multisensor fusion systems 一种基于估计误差的多传感器融合系统主动感知方法
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398397
T. Mukai, M. Ishikawa
{"title":"An active sensing method using estimated errors for multisensor fusion systems","authors":"T. Mukai, M. Ishikawa","doi":"10.1109/MFI.1994.398397","DOIUrl":"https://doi.org/10.1109/MFI.1994.398397","url":null,"abstract":"An active sensing method for multisensor fusion systems with actuators is proposed. To realize active sensing with multiple sensors, i) where to position sensors, ii) how to associate data, and iii) how to fuse data should be determined, The authors propose a new method mainly concerning i). The method utilizes estimated errors of estimated values to determine optimal sensor locations where useful data are expected to be obtained and effectively associated. An algorithm to calculate nearly optimal sensor locations, instead of exact optimal locations, is also proposed to reduce calculation. As examples, the active sensing method is applied to multitarget tracking by a system with two hand-eye cameras, and visual and tactile fusion in a system with a camera and a tactile sensor. By using this method, the sensing strategy is optimized for the object of measurement.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131329761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 34
Teleoperator-aided multi-sensor data fusion for mobile robot navigation 面向移动机器人导航的遥操作辅助多传感器数据融合
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398416
S. Yeung, W. S. McMath, E. Petriu, N. Trif, C. Gal
{"title":"Teleoperator-aided multi-sensor data fusion for mobile robot navigation","authors":"S. Yeung, W. S. McMath, E. Petriu, N. Trif, C. Gal","doi":"10.1109/MFI.1994.398416","DOIUrl":"https://doi.org/10.1109/MFI.1994.398416","url":null,"abstract":"This paper discusses a \"teleoperator-aided sensor fusion\" concept based on an interactive telepresence interface which allows the teleoperator to actually be involved in the world model building.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"44 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131775229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Integrating multisensor noisy and fuzzy data 集成多传感器噪声和模糊数据
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398457
L. Hong, Gwo-Jieh Wang
{"title":"Integrating multisensor noisy and fuzzy data","authors":"L. Hong, Gwo-Jieh Wang","doi":"10.1109/MFI.1994.398457","DOIUrl":"https://doi.org/10.1109/MFI.1994.398457","url":null,"abstract":"This paper discusses centralized integration of multisensor noisy and fuzzy data, which employs both Kalman filtering and fuzzy arithmetic. Due to the property of fuzzy arithmetic, fuzziness of the parameters in a system under the extended operation will unlimitedly increase and finally reach an unacceptable range. We have previously adopted a new compression technique to solve this problem. This paper extends our work on the filtering of single sensor noisy and fuzzy data to integrating multisensor noisy and fuzzy data. An example is given to illustrate the effectiveness of the algorithm presented.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Local path planning in dynamic environments with uncertainty 动态不确定环境下的局部路径规划
Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems Pub Date : 1994-10-02 DOI: 10.1109/MFI.1994.398459
Ashraf Elnagar, A. Basu
{"title":"Local path planning in dynamic environments with uncertainty","authors":"Ashraf Elnagar, A. Basu","doi":"10.1109/MFI.1994.398459","DOIUrl":"https://doi.org/10.1109/MFI.1994.398459","url":null,"abstract":"We address the problem of planning a safe path for a mobile robot, in the presence of moving obstacles, based on local information. We consider constraints on the speed and the acceleration of the robot. Moreover, we take into account the uncertainty in estimating the velocity of each obstacle. The concept of safety is used to design a planning strategy. A path which maximizes the product of safety based on local information, attraction towards the goal, and time-to-collision, is chosen. The safety function depends on the acceleration bounds. The attraction towards goal depends on the distance from the goal. Time-to-collision depends on how far the robot is from a moving obstacle. Simulation results of this approach are presented.<<ETX>>","PeriodicalId":133630,"journal":{"name":"Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134405706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
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