Performance evaluation and statistical analysis of algorithms for ego-motion estimation

This contribution investigates algorithms for egomotion estimation from environmental features. Various formulations for solving the underlying procrustes problem exist. It is analytically shown that in the 2-D case this can be performed more efficiently compared to common implementations based on matrix decompositions. Furthermore, analytic error propagation is performed to second order which reveals a multiplicative estimator bias. A novel bias-corrected solution is proposed and evaluated in Monte Carlo simulations. Propagation of the derived error model to a representation used in the recursive trajectory reconstruction is presented and verified.