A Policy Model Based Efficient and Accurate Scene Recognition Method for Service Robot

In domestic environments, assigning scene semantic labels (scene recognition) to each node of a topological semantic map is an important task. Given the limitations of current scene recognition methods in efficiency, and accuracy for service robot, this paper proposes a scene recognition method based on a policy model. Considering the similarity of images captured from the adjacent nodes and the low-quality image caused by the uncertain node position and observation direction of the robot, we develop a policy model using a deep Q-learning network (DQN). This model enhances accuracy and efficiency by deciding whether to (1) inherit the scene type from the preceding node without re-recognition or (2) adjust the robot's observation angle to capture a more informative image. A rule-based reward function integrated with a scene score model enables simultaneous learning of similarity assessment and viewpoint adjustment policies. Furthermore, a training strategy based on generated path is proposed to provide sufficient data for training the policy model. Extensive comparative experiments in simulated environments demonstrate that our method surpasses state-of-the-art approaches in both recognition accuracy and efficiency. Deployment on a mobile robot confirms its practical efficacy, achieving precise and efficient scene recognition across diverse real-world environments.