Multi-session perception-aware coverage path planning for active semantic SLAM and automatic change detection

This paper introduces a novel framework for multi-session, perception-aware coverage path planning integrated with active semantic Simultaneous Localization and Mapping (SLAM) and automatic change detection. The goal is to enhance autonomous robotic exploration in dynamic environments by combining semantic understanding with adaptive path planning for long-term monitoring. The proposed approach consists of three tightly integrated components. First, a semantic-informed inspection planner uses Kernel Density Estimation (KDE) to prioritize exploration of semantically significant regions. Second, an active semantic SLAM module builds a semantic map incrementally, providing real-time feedback to refine the inspection path. Third, a multi-session change detection strategy compares current and previous semantic data to identify and localize environmental changes. Together, these components allow the robot to intelligently adapt its exploration strategy over time, focusing on areas of interest and reacting to environmental dynamics. The framework is validated through both simulation and real-world experiments, demonstrating improved coverage efficiency, mapping accuracy, and change detection robustness compared to traditional methods. While applied to buoy detection, the system is broadly applicable to long-term robotic tasks such as environmental monitoring, infrastructure inspection, mine counter-measure operations, and disaster response, or other scenarios that demand adaptability and semantic awareness in complex, evolving environments.