Volumetric Instance-Level Semantic Mapping Via BlendMask

Abstract

Advanced tasks such as planning and scene interaction for autonomous robots require a detailed instance-level semantic map of the environment. To this end, this paper proposes a new volumetric instance-level semantic mapping approach, in which BlendMask is introduced as the instance segmentation algorithm for RGB images. As a result, improvements in the quality and speed of the instance segmentation are observed. Specifically, the geometric segmentation for the depth image and the instance segmentation results are fused together to construct the geometrically and semantically unified instances. Then, cross-frame instances are tracked and matched through data association. Based on the above, a global instance-level semantic map is constructed. Comparative experiments on public datasets are conducted to show that the proposed instance-level semantic mapping approach can effectively improve the instance segmentation effect and the quality of the constructed instance-level semantic map.