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Image Sequence Matching Using Both Holistic and Local Features for Loop Closure Detection

Simultaneous localization and mapping (SLAM) has a wide range of applications, such as mobile robots, intelligent vehicle localization, and intelligent transportation system. However, loop closure detection is a challenge task for SLAM. This task concerns the difficulty of recognizing already mapped areas. To this end, this paper proposes a novel loop closure detection method called image sequence matching (ISM), which only uses a low-cost monocular camera. This method first divides the already mapped areas into some “feature-zones.” One feature-zone is selected by a novel topological detection model. Then, we adopt two different feature spaces to make sequence matching between query image and feature-zone. Last but not least, we propose a novel clustering method called voting K-nearest neighbor to fuse candidates. As a result, the ISM method has been validated by using collection data sets and public data sets, which were collected along different routes, covering different times and weather conditions. The total lengths of these routes are more than 10 km. Experimental results show that the ISM method can adapt to different times with good detection stability in varying scenarios. The mean of detection errors is all less than 1 frame and the detection accuracies are all more than 90% in these scenarios. Compared with other methods, the proposed method has high accuracy and great robustness.