Robust State Estimation with Sparse Outliers

One of the major challenges for state estimation algorithms, such as the Kalman lter, is the impact of outliers that do not match the assumed Gaussian process and measurement noise. When these errors occur they can induce large state estimate errors and even lter divergence. This paper presents a robust recursive ltering algorithm, the l1-norm lter, that can provide reliable state estimates in the presence of both measurement and state propagation outliers. The algorithm consists of a convex optimization to detect the outliers followed by a state update step based on the results of the error detection. Monte Carlo simulation results are presented to demonstrate the robustness of the l1-norm lter estimates to both state prediction and measurement outliers. Finally, vision-aided navigation experimental results are presented that demonstrate that the proposed algorithm can provide improved state estimation performance over existing robust ltering approaches.