{"title":"A multisensor fusion approach to shape estimation using a mobile platform with uncalibrated position","authors":"Ziang Zhang, B. K. Ghosh","doi":"10.1109/MFI.1999.815990","DOIUrl":"https://doi.org/10.1109/MFI.1999.815990","url":null,"abstract":"Estimation of three dimensional shapes is a difficult problem which has been considered by researchers in machine vision and robotics. A typical problem is to estimate shape using images taken from various viewpoints. We consider a camera mounted on a mobile platform which is actively controlled effecting changes in the viewpoint of the camera. The mobile platform also has the capability of 2D ranging in addition to the camera-hence providing an opportunity for sensor fusion. In this paper, we first developed a new technique to calibrate the position of the mobile platform based on range lines and odometer. Then based on these calibration parameters, the shape (plane) estimation using extended Kalman filter (EKF) is carried out utilizing multiple views from the CCD camera and the range data from the range finder. The effectiveness of this approach has been proved by experimental results.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127162155","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}
{"title":"Analysis of ion and X-ray time series measurements of electron beam welding for development of online monitoring","authors":"Choong Sup Yuon, Bong Jo Hyu","doi":"10.1109/MFI.1999.815993","DOIUrl":"https://doi.org/10.1109/MFI.1999.815993","url":null,"abstract":"Time series analysis results show the singular value decomposition is a candidate of online monitoring of welding penetration in a case that the covariance matrix of a full penetration is used as a mapping function. The mapped time series lie on a hyper-ellipsoid in which the lengths of semi-axes are the squared eigenvalues of the covariance matrix. Furthermore, this algorithm is fast compared with others since only the multiplication with the covariance matrix is used. A depth of welding is closely related with the volume of hyper-ellipsoid.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121682054","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}
{"title":"An efficient and compact integration of CMOS image sensors and cellular neural network (CNN) for intelligent processing","authors":"Chung-Yu Wu, Wen-Cheng Yen","doi":"10.1109/MFI.1999.815995","DOIUrl":"https://doi.org/10.1109/MFI.1999.815995","url":null,"abstract":"By using the neuron-bipolar junction transistor (BJT) as phototransistor and single-transistor neuron, the cellular neural network (CNN) can be compactly integrated with CMOS image sensors so that the optical images can be input to the CNN directly for neural image processing. With the neuron-BJT, realized by the parasitic pnp BJT in n-well CMOS technology, the optical-input CNN with symmetric templates can be implemented in very small chip area. The cell area can be as small as 20 /spl mu/m/spl times/24 /spl mu/m. The simulation results have confirmed the correct function of the proposed optical-input compact CNN.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126440907","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}
{"title":"System modeling and robust design of microaccelerometer using piezoelectric thin film","authors":"Jyh-Cheng Yu, Chiang-Bin Lan","doi":"10.1109/MFI.1999.815972","DOIUrl":"https://doi.org/10.1109/MFI.1999.815972","url":null,"abstract":"This paper starts from the structure analysis and system modeling of piezoelectric microaccelerometer using piezoelectric thin film. The simulation model demonstrates the interaction of structure variables, piezoelectric material parameters and amplification circuit design to the sensor performance. However, manufacturing error results in the variations of design variables, which has significant effects on sensor accuracy. The proposed system model is applied to the parameter design of microaccelerometer. The paper conducts the design optimization and robustness analysis using Taguchi's method to reduce the sensitivity of the sensor response to the dimensional errors and variations of material properties. A FEM study is applied to verify the theoretical model and design improvement.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128105425","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}
{"title":"Multi-sensor/knowledge fusion","authors":"C. Hiransoog, C. Malcolm","doi":"10.1109/MFI.1999.815975","DOIUrl":"https://doi.org/10.1109/MFI.1999.815975","url":null,"abstract":"This paper presents a new approach to solve the sensor fusion problem. This new approach is called multi-sensor/knowledge fusion (MSKF) which takes into account not only the fusion of information between multiple sensory systems but also the fusion between sensory information with system's prior knowledge of its environment. This MSKF architecture is a modification of the real-time control system (RCS) developed by Albus (1997). This RCS was designed to be a reference control architecture model for creating intelligent systems. The RCS itself uses a hierarchical control strategy with each layer of the hierarchy decomposes into plans for parallel and sequential sub-tasks. However, the sensory processing module in the RCS was re-designed in the MSKF architecture for the use of multiple types of sensors. The test of this new control architecture is carried out on a LEGO toy kit assembly task.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121929829","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}
{"title":"IMMPDA filter via perception net","authors":"J. Choi, Tao Fang, S. Lee","doi":"10.1109/MFI.1999.816001","DOIUrl":"https://doi.org/10.1109/MFI.1999.816001","url":null,"abstract":"This paper presents an improved performance of the IMMPDA (interacting multiple model with probabilistic data association) filter with the detecting capability of a maneuvering. In order to detect the maneuvering of a target, an error monitoring and recovery method of perception net is applied to the IMMPDA filter. This method is derived as an extension of the previous results given by Choi et al. (1999) on tracking for a maneuvering target without clutter. The effectiveness of the proposed method is validated through simulations by comparing it with the conventional IMMPDA filter.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129332355","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}
{"title":"The sensor fusion of gyro controller system for the smart vehicle","authors":"DukSun Yun, Jung Ha Kim","doi":"10.1109/MFI.1999.815987","DOIUrl":"https://doi.org/10.1109/MFI.1999.815987","url":null,"abstract":"Nowadays, the GPS (Global Positioning System) is generally used for the car navigation system but it has some restrictions such as the discontinuity of Earth satellite's signals and artificial errors called SA (selective availability). To solve these problems, the INS (inertial navigation system) sensor fusion with GPS is very attractive for improving the accuracy of detecting a vehicle position. The DR (dead reckoning), which is one form of the INS uses a wheel magnetic sensor for the driving distance and a gyro sensor for the vehicle orientation. The black-box, which is the most important record after a car accident, contains data from acceleration sensors and a gyro sensor. In this paper, gyro controller system using single-chip microprocessor (i80c196kc) is developed for the DR and black-box.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130169448","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}
{"title":"A multisensor system for observation of user actions in programming by demonstration","authors":"M. Ehrenmann, P. Steinhaus, R. Dillmann","doi":"10.1109/MFI.1999.815981","DOIUrl":"https://doi.org/10.1109/MFI.1999.815981","url":null,"abstract":"Good observation of a manipulation presentation performed by a human teacher is crucial to further processing steps in programming by demonstration in interactive robot programming. This paper outlines a concept of how this can be done using visual and finger measuring sensors. The input sources include: a data glove which classifies several gestures and grasps, an active stereo vision, and a fixed ceiling camera. The hardware used is presented together with the technical concepts of processing and the acquired sensor information fusion, so called elementary cognitive operators. All the sensor sources use time-efficient algorithms, since sensor data must be processed in real-time.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127789681","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}
{"title":"Acquiring manipulation skills through observation","authors":"H. Tominaga, K. Ikuuchi","doi":"10.1109/MFI.1999.815956","DOIUrl":"https://doi.org/10.1109/MFI.1999.815956","url":null,"abstract":"Currently, most robot programming is done either by manual programming or using a teach pendant as part of the \"teach-by-showing\" method. Both of these methods have been found to have several drawbacks. To solve the problems, the assembly-plan-from-observation (APO) method was proposed, which has the capability of observing a human performing an assembly task, understanding the task, and subsequently generating a robot program to achieve the same task. This system, however, cannot observe a trajectory of a human performance. Necessary trajectories are generated from CAD models. Later, in order to overcome this problem, a direct observation method based on a trajectory of a human performance was proposed to project human trajectory to robot trajectory. Though its implementation is relatively easy, the system is susceptive against observation noise. This paper proposes a method to make the robust observation against noise using symbolic representations such as face contact transitions. The system divides the trajectory into small segments based on the face contact analysis, allocates an operation element, referred to as sub-skill, to those segments. By using this system, we can decompose large motion templates, employed in the previous system, into sets of smaller sub-skills.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131517334","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}
{"title":"Sensor space discretization in autonomous agent based on entropy minimization of behavior outcomes","authors":"T. Yairi, K. Hori, S. Nakasuka","doi":"10.1109/MFI.1999.815974","DOIUrl":"https://doi.org/10.1109/MFI.1999.815974","url":null,"abstract":"Sensor space discretization is a significant issue for the realization of the autonomous agents which are expected to decide and learn the proper behavior with various kinds of sensor information. This paper proposes a new sensor space discretization method based on entropy minimization of the agent's behavior outcomes. This framework unifies a variety of heuristic discretization policies used in the previous works, and provides a more general insight into this problem. An experimental study is also presented in the latter part, which suggests that our sensor discretization method greatly increases the adaptability of the agents to the environment when combined with existing behavior learning methods such as Q-Learning.","PeriodicalId":148154,"journal":{"name":"Proceedings. 1999 IEEE/SICE/RSJ. International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI'99 (Cat. No.99TH8480)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133342918","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}