BoRF: Loop-closure detection with scale invariant visual features

In this paper, we present a novel method for visual loop-closure detection in autonomous robot navigation. Our method, which we refer to as bag-of-raw-features or BoRF, uses scale-invariant visual features (such as SIFT) directly, rather than their vector-quantized representation or bag-of-words (BoW), which is popular in recent studies of the problem. BoRF avoids the offline process of vocabulary construction, and does not suffer from the perceptual aliasing problem of BoW, thereby significantly improving the recall performance. To reduce the computational cost of direct feature matching, we exploit the fact that images in the case of robot navigation are acquired sequentially, and that feature matching repeatability with respect to scale can be learned and used to reduce the number of the features considered for matching. The proposed method is tested experimentally using indoor visual SLAM image sequences.