Feature Guided Visual SLAM for Floor Cleaning Robot Path Planning

This paper proposes a feature-guided visual Simultaneous Localization and Mapping (SLAM) path planning algorithm to address challenges in traditional vacuum robot navigation, including multi-sensor dependence, dynamic adaptability, and computational inefficiency. A dynamic feature optimization mechanism and hierarchical recursive exploration strategy are integrated into the ORB-SLAM framework to construct geometric feature-driven topological maps. Key innovations include a curvature-based corner selection model for reducing redundancy and a three-layer decision architecture (feature perception, path generation, termination judgment) with polar angle sorting and key node backtracking to overcome local optima. Simulations demonstrate significant reductions in path repetition and mapping time compared to conventional methods, while maintaining compatibility with low-cost monocular vision sensors. This work provides a lightweight, feature-driven paradigm for service robot navigation.