Abstract
The joint optimization of map management and map feature to measurement association, together with the trajectory and map states, within a single, unified, Bayesian, feature-based, simultaneous localization and mapping (SLAM) solution is addressed in this article. Remarkable progress in feature-based SLAM has been made in which, given data association, the SLAM problem can be solved by use of nonlinear least squares solvers, often referred to as the SLAM back-end. These methods rely on external methods to solve both the data association and map management problems, which are collectively incorporated into the SLAM front-end. SLAM convergence failures are common when these front-end routines fail, particularly when feature detection uncertainty increases. Therefore, this article introduces Joint, Vector-Set SLAM (JVS-SLAM), utilizing Bayes theorem to solve feature to measurement association, map management, and SLAM itself jointly, thus combining the SLAM back and front ends. Results will demonstrate equivalent or superior SLAM performance to state-of-the-art solutions, under varying odometry, spatial and detection measurement uncertainties, without reliance on data association decisions. Results are based on both simulations and the challenging EuRoC data set, in which a drone undergoing high accelerations, equipped with a stereo camera, performs SLAM. Since JVS-SLAM jointly provides a solution to the map feature to measurement association problem, its computational complexity is comparable with multi-hypothesis based solutions. Parallels between state-of-the-art map management and feature to measurement association methods and the detection statistics used within JVS-SLAM will be examined, with a view to reducing its complexity in the future.
Published Version
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