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.
Highlights
Self-localization is an important part of the autonomous mobile robot, especially the indoor service robot
Comprehensive experiments were conducted, and the results demonstrated that the proposed method outperforms the visual odometry in accuracy and vision-based simultaneous localization and mapping (VSLAM) in efficiency
An indoor experiment was implemented to verify the effectiveness of the visual odometry
Summary
Self-localization is an important part of the autonomous mobile robot, especially the indoor service robot. Accurate position is necessary for navigation and trajectory planning, which play important roles in the mobile robot working in unknown indoor environment. Various localization methods have been proposed, such as Lidar, ultrasonic, WIFI, and Ultra Wideband. The vision-based localization has been intensively researched. Visual localization generally detects the existing features in the environment ranging from the artificial markers to geometric features such as corners and wall.[1,2] The position and orientation are determined by the corresponding features in the former and current frames.[3]
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