Improving Dense Mapping for Mobile Robots in Dynamic Environments Based on Semantic Information

Abstract

In recent decades, semantic mapping has become a hot topic benefited from the maturity of visual simultaneous localization and mapping (visual SLAM) and the success of deep learning. Despite the impressive performance of the current state-of-the-art systems, semantic mapping in dynamic environments is still a challenging task. To address this problem, we propose a framework that fuses geometric information, semantic information, and human activity into a 3D dense map. The accuracy of the map is guaranteed by the reliable camera trajectory estimation and the static pixels used for 3D reconstruction. With the proposed framework, we achieve two objectives. On the one hand, we accurately reconstruct the environment from both geometric and semantic perspectives. On the other hand, we record human activity by tracking the human trajectory during the mapping period. We conduct both qualitative and quantitative experiments on the public TUM dataset. The experimental results demonstrate the feasibility and effectiveness of the proposed framework.