Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images

This paper presents the novel paradigm of a global localization method motivated by human visual systems (HVSs). HVSs actively use the information of the object recognition results for self-position localization and for viewing direction. The proposed localization paradigm consisted of three parts: panoramic image acquisition, multiple object recognition, and grid-based localization. Multiple object recognition information from panoramic images is utilized in the localization part. High-level object information was useful not only for global localization, but also for robot-object interactions. The metric global localization (position, viewing direction) was conducted based on the bearing information of recognized objects from just one panoramic image. The feasibility of the novel localization paradigm was validated experimentally

Recognition-based Indoor Topological Navigation Using Robust Invariant Features

Abstract – In this paper, we present a recognition-based autonomous navigation system for mobile robots. The system is based on our previously proposed Robust Invariant Feature (RIF) detector. This detector extracts highly robust and repeatable features based on the key idea of tracking multiscale interest points and selecting unique representative local structures with the strongest response in both spatial and scale domains. Weighted Zernike moments are used as the feature descriptor and applied to the place recognition. The navigation system is composed of on-line and off-line two stages. In the off-line learning stage, we train the robot in its workspace by just taking several images of representative places as landmarks. Then, in the on-line navigation stage, the robot recognizes scenes, obtains robust feature correspondences, and navigates the environment autonomously using the Iterative Pose Converging (IPC) algorithm which is based on the idea of the visual servoing technique. The experimental results and the performance evaluation show that the proposed navigation system can achieve excellent performance in complex indoor environments. Index Terms – Object recognition, autonomous navigation, path planning, iterative pose converging, visual servoing