Scalable SLAM building conditionally independent local maps

Local maps algorithms have demonstrated to be well suited for mapping large environments as can reduce the computational cost and improve the consistency of the final estimation. In this paper we present a new technique that allows the use of local mapping algorithms in the context of EKF SLAM but without the constrain of probabilistic independence between local maps. By means of this procedure, salient features of the environment or vehicle state components as velocity or global attitude, can be shared between local maps without affecting the posterior joining process or introducing any undesirable approximations in the final global map estimate. The overload cost introduced by the technique is minimum since building up local maps does not require any additional operations apart from the usual EKF steps. As the algorithm works with covariance matrices, well-known data association techniques can be used in the usual manner. To test the technique, experimental results using a monocular camera in an outdoor environment are provided. The initialization of the features is based on the inverse depth algorithm.