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Robust factor graph optimization - a comparison for sensor fusion applications | IEEE Conference Publication | IEEE Xplore

Robust factor graph optimization - a comparison for sensor fusion applications


Abstract:

While many applications of sensor fusion suffer from the occurrence of outliers, a broad range of outlier robust graph optimization techniques has been developed for simu...Show More

Abstract:

While many applications of sensor fusion suffer from the occurrence of outliers, a broad range of outlier robust graph optimization techniques has been developed for simultaneous localization and mapping. In this paper we investigate the performance of some of the most advanced algorithms for a simulated wireless localization setting affected by non-Gaussian errors. With this first analysis we can show some of the advantages and disadvantages that are connected with the different concepts behind Max-Mixture, Generalized iSAM, Switchable Constraints and Dynamic Covariance Scaling.
Date of Conference: 06-09 September 2016
Date Added to IEEE Xplore: 07 November 2016
ISBN Information:
Conference Location: Berlin, Germany

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