Online Object-Oriented Semantic Mapping and Map Updating

TL;DR

This paper introduces a real-time, modular semantic mapping system for indoor service robots that updates object information online using RGB-D data, improving map accuracy and robustness.

Contribution

The novel system enables online object detection, shape refinement, and existence likelihood management, outperforming previous methods in accuracy and efficiency.

Findings

Achieves over 10 Hz processing speed.

Produces maps close to ground truth.

Outperforms existing approaches in accuracy metrics.

Abstract

Creating and maintaining an accurate representation of the environment is an essential capability for every service robot. Especially for household robots acting in indoor environments, semantic information is important. In this paper, we present a semantic mapping framework with modular map representations. Our system is capable of online mapping and object updating given object detections from RGB-D data and provides various 2D and 3D~representations of the mapped objects. To undo wrong data associations, we perform a refinement step when updating object shapes. Furthermore, we maintain an existence likelihood for each object to deal with false positive and false negative detections and keep the map updated. Our mapping system is highly efficient and achieves a run time of more than 10 Hz. We evaluated our approach in various environments using two different robots, i.e., a Toyota HSR and a Fraunhofer Care-O-Bot-4. As the experimental results demonstrate, our system is able to generate maps that are close to the ground truth and outperforms an existing approach in terms of intersection over union, different distance metrics, and the number of correct object mappings