Good Image Features for Bearing-only SLAM

In this paper, we propose an algorithm for extracting and selecting SIFT (scale-invariant feature transform) visual features for bearing-only SLAM in indoor environments. The algorithm is based on analyzing the stability of the matching ratio of the SIFT features at different scales, and it is capable of extracting SIFT features that can be matched reliably and, at the same time, lead to accurate landmark initialization. In addition, the algorithm is an order of magnitude more efficient than the original SIFT algorithm and is therefore appropriate for the real-time nature of SLAM. As well, the algorithm can determine the quality of the visual features without any delay, and this eliminates the need for a matching or tracking procedure, as is often necessary in other feature extraction algorithms. Results from several experiments verify the performance of the proposed algorithm