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001463151 001__ 1463151
001463151 003__ OCoLC
001463151 005__ 20230601003307.0
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001463151 019__ $$a1379724877
001463151 020__ $$a9783031240171$$qelectronic book
001463151 020__ $$a3031240170$$qelectronic book
001463151 020__ $$z9783031240157
001463151 020__ $$z3031240154
001463151 0247_ $$a10.1007/978-3-031-24017-1$$2doi
001463151 035__ $$aSP(OCoLC)1375679996
001463151 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dUKAHL$$dYDX
001463151 049__ $$aISEA
001463151 050_4 $$aTJ211.3$$b.S86 2023
001463151 08204 $$a629.8/92637$$223/eng/20230413
001463151 1001_ $$aSünderhauf, Niko,$$eauthor.
001463151 24510 $$aSwitchable constraints for robust simultaneous localization and mapping and satellite-based localization /$$cNiko Sünderhauf.
001463151 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001463151 300__ $$a1 online resource (184 pages) :$$billustrations (black and white, and color).
001463151 336__ $$atext$$btxt$$2rdacontent
001463151 337__ $$acomputer$$bc$$2rdamedia
001463151 338__ $$aonline resource$$bcr$$2rdacarrier
001463151 4901_ $$aSpringer tracts in advanced robotics ;$$vvolume 137
001463151 504__ $$aIncludes bibliographical references.
001463151 5050_ $$aSimultaneous Localization And Mapping -- Least Squares Optimization -- Motivation - When Optimization Fails -- A Robust Back-End for SLAM -- Evaluation.
001463151 506__ $$aAccess limited to authorized users.
001463151 520__ $$aSimultaneous Localization and Mapping (SLAM) has been a long-standing research problem in robotics. It describes the problem of a robot mapping an unknown environment, while simultaneously localizing in it with the help of the incomplete map. This book describes a technique called Switchable Constraints. Switchable Constraints help to increase the robustness of SLAM against data association errors and in particular against false positive loop closure detections. Such false positive loop closure detections can occur when the robot erroneously assumes it re-observed a landmark it has already mapped or when the appearance of the observed surroundings is very similar to the appearance of other places in the map. Ambiguous observations and appearances are very common in human-made environments such as office floors or suburban streets, making robustness against spurious observations a key challenge in SLAM. The book summarizes the foundations of factor graph-based SLAM techniques. It explains the problem of data association errors before introducing the novel idea of Switchable Constraints. We present a mathematical derivation and probabilistic interpretation of Switchable Constraints along with evaluations on different datasets. The book shows that Switchable Constraints are applicable beyond SLAM problems and demonstrates the efficacy of this technique to improve the quality of satellite-based localization in urban environments, where multipath and non-line-of-sight situations are common error sources.
001463151 588__ $$aDescription based on print version record.
001463151 650_0 $$aRobot vision.
001463151 655_0 $$aElectronic books.
001463151 77608 $$iPrint version:$$aSünderhauf, Niko.$$tSwitchable constraints for robust simultaneous localization and mapping and satellite-based localization.$$dCham : Springer, 2023$$z9783031240157$$w(OCoLC)1374596535
001463151 830_0 $$aSpringer tracts in advanced robotics ;$$vv. 137.
001463151 852__ $$bebk
001463151 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-24017-1$$zOnline Access$$91397441.1
001463151 909CO $$ooai:library.usi.edu:1463151$$pGLOBAL_SET
001463151 980__ $$aBIB
001463151 980__ $$aEBOOK
001463151 982__ $$aEbook
001463151 983__ $$aOnline
001463151 994__ $$a92$$bISE