Fast Loop Closure Detection Based on Holistic Features from SURF and ORB

A fast loop closure detection algorithm based on holistic features from SURF (speeded-up robust feature) and ORB (oriented FAST and rotated BRIEF) is proposed for the loop closure detection problem in mobile robot SLAM (simultaneous localization and mapping). Firstly, holistic features of a query image are extracted for feature representation by using SURF and ORB. The query image is normalized and its center is directly set as the feature point position of SURF and ORB in the process of extracting holistic features, so that the time-consuming step of feature point localization is avoided. Afterwards, the normalized image is directly used as the patch of the feature point. The local feature descriptors computed from SURF and ORB are then used as the holistic features of the image. In order to fuse the holistic features from SURF and ORB for image matching, an improved method of H-KNN (hybrid K-nearest neighbor) called WH-KNN (weighted hybrid K-nearest neighbor) is also proposed. Finally, loop closure detection is finished by a tracking model. The core idea behind the model is to utilize the previous loop closure detection results to predict the position of the query image in the map. In the experiments, the proposed algorithm is not only tested with the collected datasets and the public datasets of Oxford University, but also compared with the classic BOW (Bag of Words) algorithm. It can achieve 94.3% and 94.5% detection accuracy on these two datasets, respectively. In average, it takes less than 3 ms to match each query image and extract holistic features. The test results demonstrate that the proposed algorithm outperforms the BOW method both in term of detection accuracy and computation efficiency. Therefore, the algorithm is accurate and fast enough for real-time loop closure detection.