000 02203 a2200229 4500
003 OSt
020 _a9783031240157
040 _cIIT Kanpur
041 _aeng
082 _a629.892
_bSu72s
100 _aSünderhauf, Niko
245 _aSwitchable constraints for robust simultaneous localization and mapping and satellite-based localization
_cNiko Sünderhauf
260 _bSpringer
_c2023
_aSwitzerland
300 _axiv, 184p
440 _aSpringer tracts in advanced robotics
490 _a/ edited by Bruno Siciliano and Oussama Khatib
_v; v.137
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.
650 _aMobile robots
650 _aMappings (Mathematics)
650 _aRobotics
942 _cREF
999 _c566820
_d566820