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Including sensor bias in shape from motion calibration and sensor fusion

Shape from motion data fusion brings a greater degree of autonomy and sensor integration to intelligent systems in which fusion by constant linear transformations is appropriate. To illustrate this, we apply shape from motion techniques to applications involving both similar and disparate sensory in...

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Main Authors:	Voyles, R.M., Merrow, J.D., Khosla, P.K.
Format:	Conference Proceeding
Language:	English
Subjects:	Calibration Data mining Force sensors Intelligent sensors Intelligent systems Sensor fusion Sensor systems Shape Torque Vectors
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creator	Voyles, R.M. Merrow, J.D. Khosla, P.K.
description	Shape from motion data fusion brings a greater degree of autonomy and sensor integration to intelligent systems in which fusion by constant linear transformations is appropriate. To illustrate this, we apply shape from motion techniques to applications involving both similar and disparate sensory information vectors. First, nearly autonomous force/torque sensor calibration is demonstrated through fusion of the individual channels of raw strain gauge data. Gathering only the raw sensor signals, the motion of the force vector (the "motion") and the calibration matrix (the "shape") are simultaneously extracted by singular value decomposition. This calibration example is provided to simply explain the mathematics. Disparate sensory information is fused in a "primordial learning" mobile robot through a similar eigenspace representation. This paper summarizes these shape from motion applications and presents an extension for simultaneously extracting sensor bias.
doi_str_mv	10.1109/MFI.1996.568505
format	conference_proceeding
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This paper summarizes these shape from motion applications and presents an extension for simultaneously extracting sensor bias.</description><subject>Calibration</subject><subject>Data mining</subject><subject>Force sensors</subject><subject>Intelligent sensors</subject><subject>Intelligent systems</subject><subject>Sensor fusion</subject><subject>Sensor systems</subject><subject>Shape</subject><subject>Torque</subject><subject>Vectors</subject><isbn>078033700X</isbn><isbn>9780780337008</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1T8FKAzEUDIig1p4FT_mBXV_y-pLsUYq1CxUvCt5KNk00spstSXvw711sncsMw7zHDGN3AmohoHl4WbW1aBpVkzIEdMFuQBtA1AAfV2xeyjdMWJAQRNds3SbXH3cxffLiUxkz76ItPCZevuze85DHgQ_jIY6JO9vHLts_bdPu_yAcy-Tcsstg--LnZ56x99XT23JdbV6f2-XjpopiQYfKCeMaN9VB6DSSlEYiOAqahNfQKeeQhDYGbXAKHTkp0Xipw5RpjFI4Y_env9F7v93nONj8sz1txV_EXElL</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Voyles, R.M.</creator><creator>Merrow, J.D.</creator><creator>Khosla, P.K.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1996</creationdate><title>Including sensor bias in shape from motion calibration and sensor fusion</title><author>Voyles, R.M. ; Merrow, J.D. ; Khosla, P.K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i145t-c18c9c03330b735228230c5f751e70b6cc3517883afc63c5c2238e27fc5f98663</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Calibration</topic><topic>Data mining</topic><topic>Force sensors</topic><topic>Intelligent sensors</topic><topic>Intelligent systems</topic><topic>Sensor fusion</topic><topic>Sensor systems</topic><topic>Shape</topic><topic>Torque</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Voyles, R.M.</creatorcontrib><creatorcontrib>Merrow, J.D.</creatorcontrib><creatorcontrib>Khosla, P.K.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Voyles, R.M.</au><au>Merrow, J.D.</au><au>Khosla, P.K.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Including sensor bias in shape from motion calibration and sensor fusion</atitle><btitle>1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. 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identifier	ISBN: 078033700X
ispartof	1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242), 1996, p.93-99
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language	eng
recordid	cdi_ieee_primary_568505
source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Calibration Data mining Force sensors Intelligent sensors Intelligent systems Sensor fusion Sensor systems Shape Torque Vectors
title	Including sensor bias in shape from motion calibration and sensor fusion
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