Indoor and Outdoor Navigation based on View Sequence under Variable Illumination Condition

Abstract

We propose a view-based indoor/outdoor navigation method as an extension of the view-sequence navigation. The original view-sequence navigation method uses block matching with normalized correlation for localization, which is sensitive to local illumination changes. Therefore it is only applied for indoor environment. In this paper, we adopt accumulated block matching for improving the robustness against locally changing illumination, in which a template is divided into a set of small patches, and each patch is utilized for matching. We also propose localization criteria which help a robot decide its motion. Our experimental results demonstrate the proposed methods can be applied for indoor/outdoor environments.

Position and Rotation Estimation for Mobile Robots in Outside of Recording Path Using Ego‐Motion

“…, the View for the present position and the View for the training image sequence are matched, and the applicable View is determined. In conventional methods, the position of the matched View is estimated to be the current position of the mobile robot, and as a result, estimating the position and rotation when off the training path is difficult. Next, in the Ego‐motion estimation (II) in Fig.…”

“…, the position and rotation of the mobile robot are estimated based on the position of the matched View and the ego‐motion. Based on this method, position and rotation estimation outside of a training path, something difficult under conventional methods , can be realized, and travel along various paths can be achieved. In the following sections, the authors explain the procedure involved.…”

“…However, because the time to capture the training images and the input images differs, there is a difficulty in which an image is not matched correctly even for the same position due to changes in the background color accompanying the passage of time. Applied research in which the images are matched by using information on the robust changes in concentration (edges) for changes in the illumination in an environment has been reported on as a means to resolve the above problem. Moreover, the authors have also proposed a method to improve the estimation precision of the position while being robust with respect to changes in the environment traveled by the mobile robot by using speeded up robust features (SURF), which is robust with respect to changes in image illumination, scale, and rotation, for image referencing.…”