Improving the selection and detection of visual landmarks through object tracking
Date
2008
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The unsupervised selection and posterior recognition of visual landmarks is a highly valuable perceptual capability for a mobile robot. Recently, we proposed a system that aims to achieve this capability by combining a bottom-up data driven approach with top-down feedback provided by high level semantic representations. The bottom-up approach is based on three main mechanisms: visual attention, area segmentation, and landmark characterization. The top-down feedback is based on two information sources: i) An estimation of the robot position that reduces the searching scope for potential matches with previously selected landmarks, ii) A set of weights that, according to the results of previous recognitions, controls the influence of different segmentation algorithms in the recognition of each landmark. In this paper we explore the benefits of extending our previous work by including a visual tracking step for each of the selected landmarks. Our intuition is that the inclusion of a tracking step can help to improve the model of each landmark by associating and selecting information from its most significant views. Furthermore, it can also help to avoid problems related to the selection of spurious landmarks. Our results confirm these intuitions by showing that the inclusion of the tracking step produces a significant increase in the recall rate for landmark recognition.
Description
Keywords
Object detection, Mobile robots, Navigation, Feedback, Robotics and automation, Robustness, Computer science, Indoor environments, Global Positioning System, Buildings