Learning of Holism-Landmark Graph Embedding for Place Recognition in Long-Term Autonomy

Place recognition plays an important role to perform loop closure detection of large-scale, long-term simultaneous localization and mapping in loopy environments. The long-term place recognition problem is challenging because the environment appearance exhibits significant long-term variations across various times of the day, months, and seasons. In this letter, we introduce a novel place representation approach that simultaneously integrates semantic landmarks and holistic information to achieve place recognition in long-term autonomy. First, a graph is constructed for each place. The graph nodes encode all landmarks and the holistic image of the place scene recorded in different scenarios. The edges connecting the nodes indicate that these nodes represent the same landmark or place, even though places and landmarks encoded by the nodes may exhibit different appearances in the long-term periods. Then, a graph embedding is learned to preserve the locality in the feature descriptor space, i.e., finding a projection such that the same landmark and place have the identical representation in the new projected descriptor space, no matter in what scenarios they are recorded. We formulate the embedding learning as an optimization problem and implement a new solver that provides a theoretical convergence guarantee. Extensive evaluations are conducted using large-scale benchmark datasets of place recognition in long-term autonomy, which has shown our approach's promising performance.