An Efficient Online Loop Closure Detection System With Local Spatial Co-Occurrence Information

As a vital component in simultaneous localization and mapping techniques, appearance-based loop closure detection (LCD) plays important roles in bounding the long-term drift errors. In this article, an online LCD system based on the mutual co-occurrence information among visual features is proposed. First, a feature tracker module is designed to generate distinctive visual words, exploiting tracked words tool to improve efficiency. Then, an incrementally built vocabulary is organized by a hierarchical navigable small world graph, where the visual words are indexed. To merge a homologous word into the existing one, the vocabulary applies an improved high-dimensional online clustering method, which regards individual cluster as a normal distribution form. At the query phase, a list of candidate frames is located due to the co-occurrence constraint. Ultimately, the loop closure is specified by passing the temporal and similarity check, which avoids the memory consumption of historic image data. Validation tests based on public datasets and experimental sequences demonstrate the merit of low running time and memory cost while the high-precision performance is retained in the system.