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We propose a simple and effective method for visual loop closure detection in appearance-based robot SLAM. Unlike the Bag-of-Words (BoW hereafter) approach in most existing work of the problem, our method uses direct feature matching to detect loop closures and therefore avoid the perceptual aliasing problem caused by the vector quantization process of BoW. We show that a tree structure can be efficient in online loop closure detection. In our method, a KD-tree is built over all the key frame features and an indexing table is kept for retrieving relevant key frames. Due to the efficiency of the tree-based feature matching, loop closure detection can be achieved in real-time. To investigate the scalability of the method, we also apply the scale dependent feature selection in our method and show that the run time can be reduced significantly at the expense of sacrificing the performance to some extent. The proposed method is validated on an indoor SLAM dataset with 7,420 images.
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