Improving Semantic Mapping with Prior Object Dimensions Extracted from 3D Models

Semantic mapping is a critical challenge that must be addressed to ensure the safe navigation of mobile robots. Equipping robots with semantic information enhances their interactions with humans, as well as their navigation and task planning capabilities. Semantic maps go beyond occupancy information, providing supplementary details about mapped elements that empower robots to gain a deeper understanding of their environment. In this study, we present a real-time RGBD-based semantic mapping solution designed for autonomous mobile robots. Our proposal focuses on a specific aspect of this solution: a novel association approach to generate the 2D shape of semantic objects using prior knowledge. We evaluate our approach in two diverse environments, employing the MIR mobile robot. Our experimental results, along with a comparison to existing approaches, demonstrate that our proposal can generate maps that closely approximate the ground truth.