A Simple Sample Consensus Algorithm to Find Multiple Models

In many applications it is necessary to describe some experimental data with one ore more geometric models. A naive approach to find multiple models consists on the sequential application of a robust regression estimator, such as RANSAC [2], and removing inliers each time that a model instance was detected. The quality of the final result in the sequential approach depends strongly on the order on which the models were found. The MuSAC method proposed in this paper discovers several models at the same time, based on the consensus of each model. To reduce bad correspondences between data points and geometric models, this paper also introduces a novel distance for laser range sensors. We use the MuSAC algorithm to find models from 2D range images on cluttered environments with promising results.