At all Costs: A Comparison of Robust Cost Functions for Camera Correspondence Outliers

Camera-based localization techniques must be robust to correspondence errors, i.e., when visual features (landmarks)are matched incorrectly. The two primary techniques to address this issue are RANSAC and robust M-estimation -- each more appropriate for different applications. This paper investigates the use of different robust cost functions for M-estimation to deal with correspondence outliers, and assesses their performance under varying degrees of data corruption. Experimental results show that using an aggressive red ascending cost function (e.g., Dynamic Covariance Scaling (DCS) or Geman-McClure (G-M)) best improves accuracy by excluding outliers almost entirely. Additionally, adjusting an error-scaling parameter for the robust cost function over the course of the optimization improves convergence with poor initial conditions.