Optimizing exploration with a new uncertainty framework for active SLAM systems

Accurate reconstruction of the environment is a central goal of Simultaneous Localization and Mapping (SLAM) systems. However, the agent’s trajectory can significantly affect estimation accuracy. This paper presents a new method to model map uncertainty in Active SLAM systems using an Uncertainty Map (UM). The UM uses probability distributions to capture the spatial distribution of map uncertainty, allowing Uncertainty Frontiers (UF) to be defined as key exploration–exploitation objectives and potential stopping criteria. In addition, the method introduces the Signed Relative Entropy (SiREn), based on the Kullback–Leibler divergence, to measure both coverage and uncertainty together. This helps balance exploration and exploitation through an easy-to-understand parameter. Unlike methods that depend on particular SLAM setups, the proposed approach is compatible with different types of sensors, such as cameras, LiDARs, and multi-sensor fusion. It also addresses common problems in exploration planning and stopping conditions. Furthermore, integrating this map modeling approach with a UF-based planning system enables the agent to autonomously explore open spaces, a behavior not previously observed in the Active SLAM literature. Code and implementation details are available as a ROS node, and all generated data are openly available for public use, facilitating broader adoption and validation of the proposed approach. • Presents a new Active SLAM uncertainty model that builds an Uncertainty Map (UM). • Presents Uncertainty Frontiers via UM boost accuracy and act as a stopping criterion. • Designs Signed Relative Entropy (SiREn) index, merging map uncertainty and coverage. • Presents a unified theoretical framework and validates it with experiments.