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22 May 2014 Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots
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Abstract
An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.
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Thomas Emter and Janko Petereit "Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots", Proc. SPIE 9121, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014, 91210O (22 May 2014); https://doi.org/10.1117/12.2050401
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