Nonlinear Graph Sparsification for SLAM

In this paper we present a novel framework for nonlinear graph sparsification in the context of simultaneous localization and mapping. Our approach is formulated as a convex minimization problem, where we select the set of nonlinear measurements that best approximate the original distribution. In contrast to previous algorithms, our method does not require a global linearization point and can be used with any nonlinear measurement function. Experiments performed on several publicly available datasets demonstrate that our method outperforms the state of the art with respect to the Kullback-Leibler divergence and the sparsity of the solution.