Real-Time Vision-based Scene Classification for Adaptive Behavior in Autonomous Mobile Robots

Humans not only perceive the environment but also comprehend it while moving through a scene. In contrast, most currently deployed autonomous mobile robots rely solely on perception for obstacle avoidance and route planning, completely neglecting scene understanding. This work introduces a unified framework that integrates real-time scene classification with obstacle avoidance to enable adaptive behavior in mobile robots, adjusting speed and direction based on the scene. We also present a novel indoor scene classification dataset to improve scene awareness. The effectiveness of the framework is validated through deployment on a mobile robot, demonstrating promising results and potential scalability to self-driving cars and autonomous vehicles. The demo video is available at: https://youtu.be/ija7tZ0ZypE