Online selection of stable visual landmarks under uncertainty

Abstract

Proposes a method to autonomously select stable visual landmarks from observed features by stereo vision and a given 2D obstacle map. The robot selects as stable landmarks vertical line segments which are distinct and on a vertical plane, because they are expected to be observed reliably from various viewpoints. Due to the vision and motion error of the robot, the observed feature positions include uncertainty. This uncertainty can be reduced by matching the detected vertical plane which includes the features to a known plane in the map. The position of a selected feature is modeled by a probabilistic distribution on the known plane. The selection and modeling process is performed online to adapt to an actual lighting and background condition which varies depending on viewpoints. When the robot moves, it uses several, less uncertain landmarks to estimate its motion. Experimental results in real scenes show the validity of the proposed method.