For successful simultaneous localization and mapping (SLAM), perception of the environment is important. This paper proposes a scheme to autonomously detect visual features that can be used as natural landmarks for indoor SLAM. First, features are roughly selected from the camera image through entropy maps that measure the level of randomness of pixel information. Then, the saliency of each pixel is computed by measuring the level of similarity between the selected features and the given image. In the saliency map, it is possible to distinguish the salient features from the background. The robot estimates its pose by using the detected features and builds a grid map of the unknown environment by using a range sensor. The feature positions are stored in the grid map. Experimental results show that the feature detection method proposed in this paper can autonomously detect features in unknown environments reasonably well.
Bibliographical noteFunding Information:
This paper was produced for the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Commerce, Industry and Energy of South Korea.
- Mobile robot
- salient features
- visual attention
ASJC Scopus subject areas
- Human-Computer Interaction
- Control and Systems Engineering
- Hardware and Architecture
- Computer Science Applications