Bionic Eye-Based Active Perception and Observation Method for Dynamic and Low-Texture Environments

Environmental perception systems for mobile robots face significant challenges in dynamic and weak-texture indoor environments. Inspired by biological vision's "active observation," this paper presents a perception method using a bionic eye. By simulating human fixation behavior through its six-degree-of-freedom gaze control, the method enables active acquisition of key environmental information. The contributions are: (1) a pedestrian motion perception and dynamic interference elimination method based on YOLOv8-Pose; (2) a real-time texture quality evaluation model combining LBP analysis and feature point stability; (3) a gaze point optimization model integrating texture distribution and pedestrian motion trends, guiding the bionic eye towards high-value areas. Experimental validation within a visual SLAM system shows that, compared to fixed-view ORB-SLAM2, our method reduces absolute trajectory error by 27.5% in dynamic scenes and 48.8% in low-texture corridors, while effectively preventing feature loss and tracking failure. This work offers new insights for achieving human-like active perception in mobile robots.