Semantic navigation with domain knowledge

TL;DR

This paper presents a system that enhances mobile robot navigation by integrating semantic domain knowledge, such as Building Information Models, into localization and path planning for improved decision making in human-made environments.

Contribution

It introduces a novel integration of BIM with ROS for semantic-aware navigation, including map merging and equipment localization in real-world scenarios.

Findings

Successful simulation validation of the system

Effective real-world deployment in buildings and construction sites

Improved navigation accuracy with semantic map integration

Abstract

Several deployment locations of mobile robotic systems are human made (i.e. urban firefighter, building inspection, property security) and the manager may have access to domain-specific knowledge about the place, which can provide semantic contextual information allowing better reasoning and decision making. In this paper we propose a system that allows a mobile robot to operate in a location-aware and operator-friendly way, by leveraging semantic information from the deployment location and integrating it to the robots localization and navigation systems. We integrate Building Information Models (BIM) into the Robotic Operating System (ROS), to generate topological and metric maps fed to an layered path planner (global and local). A map merging algorithm integrates newly discovered obstacles into the metric map, while a UWB-based localization system detects equipment to be registered back into the semantic database. The results are validated in simulation and real-life deployments in buildings and construction sites.