Map-to-Text: Local Map Descriptor

Kanji TANAKA and Shogo HANADA

Abstract-Map matching, the ability to match a local map built by a mobile robot to previously built maps, is crucialin many robotic mapping, self-localization, and simultaneous localization and mapping (SLAM) applications. In this paper, we propose a solution to the gmap-to-text (M2T)h problem, which involves the generation of text descriptions of local map content based on scene understanding to facilitate fast succinct text-based map matching. Unlike previous local feature approaches that trade discriminativity for viewpoint invariance, we develop a holistic view descriptor that is view-dependent and highly discriminative. Our approach is inspired by two independent observations: (1) The behavior of mobile robots given a local map can often be characterized by a unique viewpoint trajectory, and (2) a holistic view descriptor can be highly discriminative if the viewpoint is unique given the local map. Our method consists of three distinct steps: (1) First, an informative local map of the robotfs local surroundings is built. (2) Next, a unique viewpoint trajectory is planned in accordance with the given local map. (3) Finally, a synthetic view is described at the designated viewpoint. Because the success of our holistic view descriptor depends on the assumption that the viewpoint is unique given a local map, we also address the issue of viewpoint planning and present a solution that provides similar views for similar local maps. Consequently, we also propose a practical map-matching framework that combines the advantages of the fast succinct bag-of-words technique and the highly discriminative M2T holistic view descriptor. The results of experiments conducted using the publicly available radish dataset verify the efficacy of our proposed approach.
KeyWords : map-to-text, map matching, scene descriptor




Fig. 1. Generating text description of local map content. The top row shows four different places in the robotfs operating environment. The middle and bottom rows respectively show two independent local maps built by the robot at different time of the day that are warped into synthetic views by the proposed framework. For the sake of visualization, the viewpoint are located at the center and aligned with the uppper direction of the figure. As can be seen, similar views are produced for similar local maps. Our idea is to convert each view into a holistic view descriptor.




Fig. 2. Detection, extraction and description of 10-dim polestar features.




Fig. 3. Datasets used for experiments. gabuildingh, galberth, gfr079h, grunh, gfr101h, and gkwingh from radish dataset [10] are used as the dictionary maps.




Fig. 4. Examples of matching relevant pairs. Green and blue points respectively indicate query and database maps, while the red lines indicate correspondence found by either method. For the sake of visualization, every map in the figure is aligned by our map alignment framework.



Fig. 5. Examples of matching irrelevant pairs. Green and blue points respectively indicate query and database maps, while the red lines indicate correspondence found by either method. For the sake of visualization, every map in the figure is aligned by our map alignment framework.


Table 1 Summary of ANR performance [%].




Fig. 6. ANR performance for each dataset (horizontal axis: sorted query map ID, vertical axis: ANR in [%]). Top: BoW. Bottom: M2T.





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Fig. 7. Examples of matching M2T local map descriptors. (a) The local maps and the viewpoint trajectories. The top panel shows a collection of local maps (blue dots), the viewpoint trajectory (purple points), as well as the guniqueh viewpoints planned by our framework (blue points). The bottom panel shows the sequence of planned viewpoints in the xyt space (t: viewpoint ID). (b) Matching M2T descriptors between a relevant map pair (first and second columns) and between an irrelevant map pair (third and fourth columns). The relevant and irrelevant pairs are also indicated by purple line segments in
Fig. 7a. For each column, the matched visual words are highlighted in red. Each row shows from top to bottom, appearance word wa, pose words wx;wy, and the text description fhwx;wy;waig.


Members @@@@Tanaka Kanji, Hanada Shogo, Liu Enfu, Takahashi Yoshiki


Relevant Publication:

Shogo Hanada, Kanji Tanaka
M2T: Local map descriptor
System Integration (SII), 2014 IEEE/SICE International Symposium on, 210-215
Bibtex source, Abstract, Document PDF


Acknowledgements:

This work is supported in part by JSPS KAKENHI Grant-in-Aid for Young Scientists (B) 23700229, and for Scientific Research (C) 26330297.