Real-time indoor semantic map construction combined with the lightweight object detection network

摘要

Abstract Aiming at the lack of semantic information of indoor objects in the grid map commonly used in the indoor mobile robot, this paper proposes a real-time indoor semantic map construction method combined with the lightweight object detection network: firstly, we proposes a lightweight object detection network which is called S-SSD (ShuffleNet-SSD) by improving the SSD (Single Shot MultiBox Detector) network, it can be used to extract the semantic information of indoor objects in real time; then the semantic information of indoor objects is transformed into the grid map which is created by mobile robot, so the indoor semantic map is constructed. The effectiveness and superiority of S-SSD in the indoor object detection of mobile robot are verified by the comparative experiment, and the effectiveness of the real-time indoor semantic map construction proposed in this paper is verified by the experiment of semantic map construction.