Robust Sensor Characterization via Max-Mixture Models: GPS Sensors

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013

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Abstract

Large position errors plague GNSS-based sensors (e.g., GPS) due to poor satellite configuration and multipath effects, resulting in frequent outliers. Due to quadratic cost functions when optimizing SLAM via nonlinear least square methods, a single such outlier can cause severe map distortions. Following in the footsteps of recent improvements in the robustness of SLAM optimization process, this work presents a framework for improving sensor noise characterizations by combining a machine learning approach with max-mixture error models. By using max-mixtures, the sensor's noise distribution can be modeled to a desired accuracy, with robustness to outliers. We apply the framework to the task of accurately modeling the uncertainties of consumer-grade GPS sensors. Our method estimates the observation covariances using only weighted feature vectors and a single max operator, learning parameters off-line for efficient on-line calculation.


bibtex

@inproceedings{morton2013iros,
    TITLE      = {Robust Sensor Characterization via Max-Mixture Models: {GPS}
                 Sensors},
    AUTHOR     = {Ryan Morton and Edwin Olson},
    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
                 Robots and Systems {(IROS)}},
    YEAR       = {2013},
    MONTH      = {November},
    KEYWORDS   = {sensor characterization, max-mixtures, GPS},
}