Affiliation:
1. Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 079, 79110 Freiburg, Germany
Abstract
In this paper, we present an efficient approach to obstacle detection for humanoid robots based on monocular images and sparse laser data. We particularly consider collision-free navigation with the Nao humanoid, which is the most popular small-size robot nowadays. Our approach first analyzes the scene around the robot by acquiring data from a laser range finder installed in the head. Then, it uses the knowledge about obstacles identified in the laser data to train visual classifiers based on color and texture information in a self-supervised way. While the robot is walking, it applies the learned classifiers to the camera images to decide which areas are traversable. As we show in the experiments, our technique allows for safe and efficient humanoid navigation in real-world environments, even in the case of robots equipped with low-end hardware such as the Nao, which has not been achieved before. Furthermore, we illustrate that our system is generally applicable and can also support the traversability estimation using other combinations of camera and depth data, e.g. from a Kinect-like sensor.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Mechanical Engineering
Reference8 articles.
1. Discrete Cosine Transform
2. Scene Labeling by Relaxation Operations
3. A method of deriving compatibility coefficients for relaxation operators
4. Y. Bengio, O. Delalleau and N. Le Roux, Semi-Supervised Learning, eds. O. Chapelle, B. Schölkopf and A. Zien (MIT Press, 2006) pp. 193–216.
Cited by
19 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献