Evaluation of Local Descriptors for Vision-Based Localization of Humanoid Robots

In this paper, we address the problem of appearance-based localization of humanoid robots in the context of robot navigation using a visual memory. This problem consists in determining the most similar image belonging to a previously acquired set of key images (visual memory) to the current view of the monocular camera carried by the robot. The robot is initially kidnapped and the current image has to be compared with the visual memory. We tackle the problem by using a hierarchical visual bag of words approach. The main contribution of the paper is a comparative evaluation of local descriptors to represent the images. Real-valued, binary and color descriptors are compared using real datasets captured by a small-size humanoid robot. A specific visual vocabulary is proposed to deal with issues generated by the humanoid locomotion: blurring and rotation around the optical axis.