Error propagation on the Euclidean motion group arises in a number of areas such as in dead reckoning errors in mobile robot navigation and joint errors that accumulate from the base to the distal end of kinematic chains such as manipulators and biological macromolecules. We address error propagation in rigid-body poses in a coordinate-free way. In this paper we show how errors propagated by convolution on the Euclidean motion group, SE132, can be approximated to second order using the theory of Lie algebras and Lie groups. We then show how errors that are small (but not so small that linearization is valid) can be propagated by a recursive formula derived here. This formula takes into account errors to second order, whereas prior efforts only considered the first-order case. Our formulation is non-parametric in the sense that it will work for probability density functions of any form (not only Gaussians). Numerical tests demonstrate the accuracy of this second-order theory in the context of a manipulator arm and a flexible needle with bevel tip. KEY WORDS-recursive error propagation, Euclidean group, spatial uncertainty.
To capture the effect of large joint errors on error covariance authors in REF presented a second order theory of error propagation.
Nonparametric Second-order Theory of Error Propagation on Motion Groups
The International Journal of Robotics Research
Computer Science Medicine