Optimizing Exploration with a New Uncertainty Framework for Active SLAM Systems

2025-06-21

AI Summary

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.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 where the map is uncertain, 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.

Problem

• Modeling map uncertainty in Active SLAM using Uncertainty Map (UM)
• Balancing exploration-exploitation with Signed Relative Entropy (SiREn)
• Enabling autonomous exploration in open spaces with UF-based planning

Innovation

• UM models map uncertainty with probability distributions
• SiREn measures coverage and uncertainty together
• Compatible with various sensors like cameras and LiDARs