Monocular vision simultaneous localization and mapping using SURF

In visual SLAM features are extracted from images as landmarks. Features should be easily and fast extracted and should be reliably matched in different situations. However, most point features extraction methods used in visual SLAM cannot trade off the reliability of matching and the speed of extraction. In our monocular vision SLAM research, a new feature extractor SURF, which provides both robust matching and high speed extraction, is applied to detect point landmarks from image of indoor environment. We use EKF method to estimate the states of camera and landmarks. In addition, both a new feature detecting strategy which considers landmark distribution in the environment, and an improved landmark management mechanism which allows some landmarks existing permanently to deal with unobservable situation are proposed. The experiments demonstrate that our SLAM approach is effective and has real-time performance.