An Anonymous Extent-Informed Approach for Map-Based Localization

Abstract

The anonymous extent-informed (AEI) update for map-based localization is introduced in this article. The method is derived using a random-finite-set-based observation model, where an expression is proposed that facilitates the approximation of the generalized likelihood to a specified degree. The proposed update builds upon the anonymous feature processing (AFP) approach by specifying prior and landmark likelihood models that account for extent dependencies in the detection process. Through this construction, the impact of a landmark's spatial extent in detection can be accounted for and simultaneously used to perform a pseudoidentification of landmarks based upon the observed extent. The AEI update is applied to a lunar descent scenario, where the simulated vehicle collects optical observations of the lunar surface and compares them to an onboard crater catalog, and compared to a Gaussian mixture implementation of AFP and the standard extended Kalman filter implementation. Results indicate that the AEI update can provide more consistent estimates of the vehicle's position and velocity than the other methods, while also requiring fewer components in the posterior mixture. The AEI update is also shown to be more robust to the presence of clutter and false detection processes.