Hybrid visual natural landmark–based localization for indoor mobile robots

Abstract

Localization is a crucial part of autonomous moving for the indoor mobile robot. The natural features of the ceiling and surrounding environment can serve for position estimation. Based on these natural features, a hybrid visual natural landmarks–based localization method is proposed. We combine the landmarks-based positioning with ceiling-based visual odometry. During the visual odometry, the orientation is computed from the parallel features between the adjacent frames. The position is calculated from the corresponding point features in the two consecutive images using the perspective-n-point method. During the natural landmarks–based localization, the orientation filter is utilized to obtain the global orientation. Then, the feature points are determined by the Compute Unified Device Architecture–based scale-invariant feature transform algorithm. Finally, the position is estimated based on the computed orientation and point features. Various experiments have been conducted to evaluate the effectiveness of the proposed method. The experimental results show that the proposed localization method outperforms other methods in accuracy and efficiency.