Combining Grid Mapping with local map descriptor for fast succinct map retrieval

Map retrieval, the problem of similarity search among a large collection of 2D pointset maps previously built by mobile robots, is crucial for autonomous navigation in both indoor and outdoor environments. Unlike previous Bag-of-words scene model which lacks spatial layout information, we exploit a holistic local map descriptor which is view-independent and highly discriminative. The current study is inspired by our recent papers on “local map descriptor”, in which the viewpoint or the origin of local map coordinate is planned by scene parsing and the spatial layout of a local map is represented with respect to the planned viewpoint. The main contribution of this study is to build on the framework of local map descriptor and experimentally evaluate the effectiveness of the proposed method. The key observation is that accuracy of the spatial layout should be improved by introducing an accurate local mapping technique. To this end, we implemented the state-of-the-art particle filter-based mapping algorithm of Grid Mapping. The next contribution is that we conducted map retrieval experiments using a real mobile robot in feature-less office-like environments. We experimentally demonstrate the effectiveness of the proposed approach.