Batch heterogeneous outlier rejection for feature-poor SLAM

In this paper, the problem of outliers in a batch alignment problem (given heterogeneous measurements and sparse features) is considered. The conventional approach from the field of computer vision, pairwise RANSAC, is shown to be inappropriate for this scenario, which motivates the need for a new method. To address this problem, the heterogeneous measurements are compared in a common currency using their respective scaled measurement innovations. Furthermore, a family of three algorithms for classifying outliers given a hypothesis model are presented, each having its own balance between speed and accuracy. These classification criteria are then incorporated through iterative reclassification in a batch alignment framework, providing a robust estimate for localization and mapping. Lastly, statistical validation is obtained through a large set of simulated trials.