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We consider the visual feature selection to improve the estimation qualityrequired for the accurate navigation of a robot. We build upon a key propertythat asserts: contributions of trackable features (landmarks) appear linearlyin the information matrix of the corresponding estimation problem. We utilizestandard models for motion and vision system using a camera to formulate thefeature selection problem over moving finite time horizons. A scalablerandomized sampling algorithm is proposed to select more informative features(and ignore the rest) to achieve a superior position estimation quality. Weprovide probabilistic performance guarantees for our method. Thetime-complexity of our feature selection algorithm is linear in the number ofcandidate features, which is practically plausible and outperforms existinggreedy methods that scale quadratically with the number of candidates features.Our numerical simulations confirm that not only the execution time of ourproposed method is comparably less than that of the greedy method, but also theresulting estimation quality is very close to the greedy method.

Estimation with Fast Landmark Selection in Robot Visual Navigation