GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes

• URL: http://arxiv.org/abs/2505.20294v1
• Date: Mon, 26 May 2025 17:59:52 GMT
• Title: GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes
• Authors: Xiao Chen, Tai Wang, Quanyi Li, Tao Huang, Jiangmiao Pang, Tianfan Xue,
• Abstract summary: Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots.<n>We introduce GLEAM, a unified generalizable exploration policy for active mapping.<n>It significantly outperforms state-of-the-art methods, achieving 66.50% coverage (+9.49%) with efficient trajectories and improved mapping accuracy on 128 unseen complex scenes.
• Score: 21.208049616708042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by insufficient training data and conservative exploration strategies, exhibit limited generalizability across scenes with diverse layouts and complex connectivity. To enable scalable training and reliable evaluation, we introduce GLEAM-Bench, the first large-scale benchmark designed for generalizable active mapping with 1,152 diverse 3D scenes from synthetic and real-scan datasets. Building upon this foundation, we propose GLEAM, a unified generalizable exploration policy for active mapping. Its superior generalizability comes mainly from our semantic representations, long-term navigable goals, and randomized strategies. It significantly outperforms state-of-the-art methods, achieving 66.50% coverage (+9.49%) with efficient trajectories and improved mapping accuracy on 128 unseen complex scenes. Project page: https://xiao-chen.tech/gleam/.

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