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Paper TuP112.1

Agamennoni, Gabriel (The University of Sydney), Nieto, Juan (University of Sydney, Australian Centre for Field Robotics), Nebot, Eduardo (Unversity of Sydney)

An Outlier-Robust Kalman Filter

Scheduled for presentation during the Regular Sessions "Field and Underwater Robotics I" (TuP112), Tuesday, May 10, 2011, 13:40−13:55, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Field Robots, Sensor Fusion, Autonomous Navigation

Abstract

We introduce a novel approach for processing sequential data in the presence of outliers. The outlier-robust Kalman filter we propose is a discrete-time model for sequential data corrupted with non-Gaussian and heavy-tailed noise. We present efficient filtering and smoothing algorithms which are straightforward modifications of the standard Kalman filter Rauch-Tung-Striebel recursions and yet are much more robust to outliers and anomalous observations. Additionally, we present an algorithm for learning all of the parameters of our outlier-robust Kalman filter in a completely unsupervised manner. The potential of our approach is borne out in experiments with synthetic and real data.

 

 

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