Optimizing Exploration with a New Uncertainty Framework for Active SLAM Systems
To address the exploration-exploitation imbalance, trajectory-dependent mapping uncertainty, and lack of general stopping criteria in active SLAM, this paper proposes an active mapping framework grounded in Uncertainty Maps (UM) and Uncertainty Frontiers (UF). We introduce Signed Relative Entropy (SiREn), a novel metric that jointly quantifies spatial coverage and state uncertainty, enabling dynamic balancing between exploration and exploitation. A probabilistic UM model is developed to support heterogeneous sensors—including monocular/stereo cameras, LiDAR, and multi-sensor fusion. Furthermore, we design a UF-driven online planning algorithm coupled with an adaptive termination strategy. To our knowledge, this is the first approach achieving fully autonomous exploration in open environments, significantly improving both mapping accuracy and path efficiency. We release an open-source ROS implementation alongside benchmark real-world and simulated datasets, facilitating reproducible research and community extension.