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Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. The instantaneous state of an object is specified by the rotation, which is defined by a rotation axis and rotation angle, and the displacement of the center of rotation. We have introduced a method based on Moore\u2010Penrose pseudoinverse theory to estimate the instantaneous state of an object, and a linear feedback estimation algorithm to approach the motion estimation. The motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision. With the assumption that the motion is smooth, our approach can handle the data sequences from multiple sensors with different sampling times. We can also predict the next immediate object position and its motion. The simulation results show our proposed approach is advantageous in terms of accuracy, speed, and versatility.<\/jats:p>","DOI":"10.1002\/rob.4620070308","type":"journal-article","created":{"date-parts":[[2007,7,6]],"date-time":"2007-07-06T09:48:58Z","timestamp":1183715338000},"page":"419-443","source":"Crossref","is-referenced-by-count":4,"title":["Motion estimation of 3\u2010D objects using multisensor data fusion"],"prefix":"10.1002","volume":"7","author":[{"given":"Ren C.","family":"Luo","sequence":"first","affiliation":[]},{"given":"Woo Suk","family":"Yang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2007,3,22]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/293133a0"},{"key":"e_1_2_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1984.4767471"},{"key":"e_1_2_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1987.4767920"},{"key":"e_1_2_1_5_2","doi-asserted-by":"crossref","unstructured":"B. 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