Visual SLAM for robot navigation in healthcare facility

Fang, Baofu; Mei, Gaofei; Yuan, Xiaohui; Wang, Le; Wang, Zaijun; Wang, Junyang

The COVID-19 pandemic has affected many countries, posing a threat to human health and safety, and putting tremendous pressure on the medical system. This paper proposes a novel SLAM technology using RGB and depth images to improve hospital operation efficiency, reduce the risk of doctor-patient cross-infection, and curb the spread of the COVID-19. Most current visual SLAM researches assume that the environment is stationary, which makes handling real-world scenarios such as hospitals a challenge. This paper proposes a method that effectively deals with SLAM problems for scenarios with dynamic objects, e.g., people and movable objects, based on the semantic descriptor extracted from images with help of a knowledge graph. Specifically, our method leverages a knowledge graph to construct a priori movement relationship between entities and establishes high-level semantic information. Built upon this knowledge graph, a semantic descriptor is constructed to describe the semantic information around key points, which is rotation-invariant and robust to illumination. The seamless integration of the knowledge graph and semantic descriptor helps eliminate the dynamic objects and improves the accuracy of tracking and positioning of robots in dynamic environments. Experiments are conducted using data acquired from healthcare facilities, and semantic maps are established to meet the needs of robots for delivering medical services. In addition, to compare with the state-of-the-art methods, a publicly available dataset is used in our evaluation. Compared with the state-of-the-art methods, our proposed method demonstrated great improvement with respect to both accuracy and robustness in dynamic environments. The computational efficiency is also competitive.