Hierarchical Loop Closure Detection for Topological Mapping
This chapter describes a novel appearance-based approach for topological mapping called HTMap (Hierarchical Topological Mapping), which is based on a hierarchical decomposition of the environment. Images with similar appearances are grouped together in locations, taking as a representative of the group the average of the PHOG global descriptors of the represented images, as well as the set of their local features, which are indexed by means of OBIndex (which handles them as explained in the previous chapter). As a main innovation, the algorithm proposes a two-level approach to detect loop candidates: first, the global descriptor of the current image is used to determine the most similar location of the map; next, local image features are employed to determine the most likely image within that location.
- 1.Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: IEEE International Conference on Computer Vision, pp. 1470–1477 (2003)Google Scholar
- 3.Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: ACM International Conference on Image and Video Retrieval, pp. 401–408 (2007)Google Scholar
- 4.Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: European Conference on Computer Vision, pp. 430–443 (2006)Google Scholar
- 7.Milford, M., Wyeth, G.: SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: IEEE International Conference on Robotics and Automation, pp. 1643–1649 (2012)Google Scholar