Map-to-Text: Local Map Descriptor
Tanaka Kanji, Hanada Shogo · SICE Journal of Control Measurement and System Integration · 2016
Map matching, the ability to match a local map built by a mobile robot to previously built maps, is crucial in many robotic mapping, self-localization, and simultaneous localization and mapping (SLAM) applications. In this paper, we propose a solution to the “map-to-text (M2T)” 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 robot's 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.