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Paper ThAP.14

liu, enfu (univ of fukui), Tanaka, Kanji (University of Fukui)

Discriminative Map Matching Using View Dependent Map Descriptor

Scheduled for presentation during the Poster session "Late Breaking Posters" (ThAP), Thursday, October 1, 2015, 09:45−10:00, Saal G1

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 19, 2019

Keywords Localization, Recognition, Visual Navigation

Abstract

The problem of matching a local occupancy grid map built by a mobile robot to previously built maps is crucial for autonomous navigation in both indoor and outdoor environments. In this paper, the map matching problem is addressed from a novel perspective, which is different from the classic bag-of-words (BoW) paradigm. Unlike previous BoW approaches that trade discriminativity for viewpoint invariance, we develop a local map descriptor that is view-dependent and highly discriminative. Our method consists of three distinct steps: (1) First, an informative local map of the robot's local surroundings is built. (2) Next, a unique viewpoint 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 local map descriptor (LMD) 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 LMD descriptor. The results of experiments conducted verify the efficacy of our proposed approach.

 

 

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