{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:10:14Z","timestamp":1726200614453},"reference-count":36,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2013,4,1]],"date-time":"2013-04-01T00:00:00Z","timestamp":1364774400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2013,4]]},"abstract":" In this paper, we present Gaussian Process Gauss\u2013Newton (GPGN), an algorithm for non-parametric, continuous-time, nonlinear, batch state estimation. This work adapts the methods of Gaussian process (GP) regression to address the problem of batch simultaneous localization and mapping (SLAM) by using the Gauss\u2013Newton optimization method. In particular, we formulate the estimation problem with a continuous-time state model, along with the more conventional discrete-time measurements. Two derivations are presented in this paper, reflecting both the weight-space and function-space approaches from the GP regression literature. Validation is conducted through simulations and a hardware experiment, which utilizes the well-understood problem of two-dimensional SLAM as an illustrative example. The performance is compared with the traditional discrete-time batch Gauss\u2013Newton approach, and we also show that GPGN can be employed to estimate motion with only range\/bearing measurements of landmarks (i.e. no odometry), even when there are not enough measurements to constrain the pose at a given timestep. <\/jats:p>","DOI":"10.1177\/0278364913478672","type":"journal-article","created":{"date-parts":[[2013,5,17]],"date-time":"2013-05-17T15:08:23Z","timestamp":1368803303000},"page":"507-525","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":39,"title":["Gaussian Process Gauss\u2013Newton for non-parametric simultaneous localization and mapping"],"prefix":"10.1177","volume":"32","author":[{"given":"Chi Hay","family":"Tong","sequence":"first","affiliation":[{"name":"Autonomous Space Robotics Lab, University of Toronto Institute for Aerospace Studies, Toronto, ON, Canada"}]},{"given":"Paul","family":"Furgale","sequence":"additional","affiliation":[{"name":"Autonomous Systems Lab, ETH Z\u00fcrich, Switzerland"}]},{"given":"Timothy D.","family":"Barfoot","sequence":"additional","affiliation":[{"name":"Autonomous Space Robotics Lab, University of Toronto Institute for Aerospace Studies, Toronto, ON, Canada"}]}],"member":"179","published-online":{"date-parts":[[2013,5,17]]},"reference":[{"key":"bibr1-0278364913478672","first-page":"1","volume":"1","author":"Archambeau C","year":"2007","journal-title":"Journal of Machine Learning Research: Workshop and Conference Proceedings"},{"key":"bibr2-0278364913478672","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2010.5509262"},{"key":"bibr3-0278364913478672","series-title":"Technical Report","volume-title":"A Solution to the General Problem of Multiple Station Analytical Stereotriangulation","volume":"43","author":"Brown 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