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Stochastic Cloning and Smoothing for Fusion of Multiple Relative and Absolute Measurements for Localization and Mapping

 
: Emter, Thomas; Petereit, Janko

:
Postprint urn:nbn:de:0011-n-5376973 (84 KByte PDF) - Die Publikation wurde zurückgezogen und durch eine neue Version ersetzt.
MD5 Fingerprint: 861926eedcd139e5340fad426727ce42
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 26.3.2019

Postprint urn:nbn:de:0011-n-537697-10 (2.4 MByte PDF)
MD5 Fingerprint: 9977870e5bc0fe078feaed129b41563c
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 1.5.2019


Institute of Electrical and Electronics Engineers IEEE; IEEE Control Systems Society -CSS-:
15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018 : 18-21 Nov. 2018, Singapore
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-9583-8
ISBN: 978-1-5386-9582-1
ISBN: 978-1-5386-9581-4
pp.1508-1513
International Conference on Control, Automation, Robotics and Vision (ICARCV) <15, 2018, Singapore>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
navigation; localization; mapping; SLAM; stochastic cloning; smoothing; loop closure

Abstract
A mobile robot is reliant on precise and robust localization and mapping for autonomous navigation. For this purpose, sensor fusion techniques are employed to combine measurements of multiple sensor data sources. The well-known Extended Kalman filter is the standard approach to integrate absolute measurements; however, multiple relative measurements, i.e., measured differences between the current system state and a past system state, cannot be directly incorporated into the filter. This paper presents a fusion algorithm for the integration of absolute and multiple relative measurements for localization and mapping of mobile robots. A novel approach exploiting concurrent stochastic cloning and smoothing is introduced for robust inclusion of additional relative measurements. The proposed fusion method is applied to perform simultaneous localization and mapping with sensor data from an IMU, a GPS, wheel odometry, and scan matching of data from a 3D LiDAR.

: http://publica.fraunhofer.de/documents/N-537697.html