Semantic world modelling and indoor navigation using OpenStreetMap

Abstract In this work a graph-based, semantic mapping approach for indoor robotics applications is presented, which is extending OpenStreetMap (OSM) with robotic-specific, semantic, topological, and geometrical information. Models are introduced for basic indoor structures such as walls, doors, corridors, elevators, etc. The architectural principles support composition with additional domain and application-specific knowledge. As an example, a model for an area is introduced, and it is explained how this can be used in navigation. A key advantage of the proposed graph-based map representation is that it allows exploiting the hierarchical structure of the graphs. Finally, the compatibility of the approach with existing, grid-based motion planning algorithms is shown.

See our ICRA 2019 paper for more details: Download paper

This research was funded by the European Unions Horizon 2020 ROPOD project.

Here is the video demonstrating the use of proposed approach for multi-floor cart transportation usecase in the ROPOD project:

Semantic navigation - proof of concept

Semantic navigation exploits the knowledge in the semantic map to select and configure perception and motion control algorithms and effectively turns the localization and navigation task into a sequence of perception and control problems. A solution for each individual perception and control problem is selected from a set of software components. The assumptions and outputs of each componet are continuously momitored to determine the transitions to new components.