Analysis of Robust Functions for Registration Algorithms
This work addresses the tedious selection of outlier filters for registration in mobile robotics, providing an incremental analysis to guide algorithm choice.
This paper tackles the problem of selecting outlier filters for registration algorithms in mobile robotics by conducting a large-scale comparison of 14 common filters, including M-estimators, across over two million registrations, finding that most perform similarly when tuned correctly, with Var. Trim., Cauchy, and Cauchy MAD being more stable and L1 norm achieving comparable accuracy without parameters.
Registration accuracy is influenced by the presence of outliers and numerous robust solutions have been developed over the years to mitigate their effect. However, without a large scale comparison of solutions to filter outliers, it is becoming tedious to select an appropriate algorithm for a given application. This paper presents a comprehensive analyses of the effects of outlier filters on the ICP algorithm aimed at mobile robotic application. Fourteen of the most common outlier filters (such as M-estimators) have been tested in different types of environments, for a total of more than two million registrations. Furthermore, the influence of tuning parameters have been thoroughly explored. The experimental results show that most outlier filters have similar performance if they are correctly tuned. Nonetheless, filters such as Var. Trim., Cauchy, and Cauchy MAD are more stable against different environment types. Interestingly, the simple norm L1 produces comparable accuracy, while been parameterless.