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Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in position over time, known as the static-world assumption. This is rarely, if ever, the case in most real-world environments. Even worse, over long deployments, robots are bound to observe traditionally static landmarks change, for example when an autonomous vehicle encounters a construction zone. This work addresses this challenge, accounting for changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks. To accomplish this, landmarks are clustered into cliques and a filter is developed to estimate their persistence jointly among observations of the landmarks in a clique. This filter uses estimated spatial-temporal priors of geometric objects, allowing for dynamic and semi-static objects to be removed from a formally static map. The proposed algorithm is validated in a 3D simulated environment. 

This paper is concerned with system identification and the calibration of parameters of dynamic models used in different robotic platforms. A constant time algorithm has been developed in order to automatically calibrate the parameters of a high-fidelity dynamical model for a robotic platform. The presented method is capable of choosing informative motion segments in order to calibrate model parameters in constant time while also calculating a confidence level on each estimated parameter. Simulations and experiments with a 1/8th scale four wheel drive vehicle are performed to calibrate two of the parameters of test vehicle which demonstrate the accuracy and efficiency of the approach.