Characterizing the Uncertainty of Jointly Distributed Poses in the Lie\n Algebra Article Swipe
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1906.07795
· OA: W4288321962
An accurate characterization of pose uncertainty is essential for safe\nautonomous navigation. Early pose uncertainty characterization methods proposed\nby Smith, Self, and Cheeseman (SCC), used coordinate-based first-order methods\nto propagate uncertainty through non-linear functions such as pose composition\n(head-to-tail), pose inversion, and relative pose extraction (tail-to-tail).\nCharacterizing uncertainty in the Lie Algebra of the special Euclidean group\nresults in better uncertainty estimates. However, existing approaches assume\nthat individual poses are independent. Since factors in a pose graph induce\ncorrelation, this independence assumption is usually not reflected in reality.\nIn addition, prior work has focused primarily on the pose composition\noperation. This paper develops a framework for modeling the uncertainty of\njointly distributed poses and describes how to perform the equivalent of the\nSSC pose operations while characterizing uncertainty in the Lie Algebra.\nEvaluation on simulated and open-source datasets shows that the proposed\nmethods result in more accurate uncertainty estimates. An accompanying C++\nlibrary implementation is also released.\n This is a pre-print of a paper submitted to IEEE TRO in 2019.\n
