A simple and reliable keypoint matching method is proposed in this paper. Our research is motivated by the desire to improve the performance of multi-view geometry (MVG) based verification in visual loop closure detection under significant illumination change, where traditional methods may fail due to their inability to either find a sufficient number of correctly matched keypoints or identify correct underlying camera motion to verify the matches. Our method is inspired by research on the spatial statistics of optical flow. By observing that the displacement of matching keypoints between a pair of images is equivalent to the optical flow under the assumption of small camera motion (which is true in applications such as loop closure detection), we exploit the fact that the displacement of correctly matched keypoints between two images must follow a well-defined distribution. This paves the way to a keypoint matching method that uses this distribution to screen or prune potential matching keypoints, so as to remove the incorrect matches (outliers) and retain the true matches (inliers) without being overly and solely dependent on keypoint descriptors. The proposed method is validated on the outdoor image sequences and shows superior performance to the standard keypoint matching method based on distance ratio test.

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.

In this paper, we present a method for visual loop closure detection using a compact image descriptor, Gabor-Gist. In contrast to the Bag-of-Words (BoW) approach, which is dominant in recent studies of the loop closure detection problem that derives an image descriptor from locally extracted keypoint descriptors, our method relies on a single efficient image descriptor of low dimension to describe and measure similarities among images. We employ PCA to transform a high dimensional Gabor-Gist descriptor to a lower dimensional form to improve both the computational efficiency of our method and the discriminative power of the image descriptor. In addition, we use a particle filter to exploit the correlation among images in a sequence captured by the robot in the process of identifying loop closure candidates. Our method is highly scalable due to the compactness of the image descriptor and the simplicity of particle filtering. To validate our method, we used the Oxford City dataset. Our experimental results show that for this dataset, high recall (up to 87%) can be obtained at 100% precision, with only a few particles.