GLEAM: Learning Generalizable Exploration Policy for Active Mapping in
  Complex 3D Indoor Scenes
Authors: name: Xiao Chen, name: Tai Wang, name: Quanyi Li, name: Tao Huang, name: Jiangmiao Pang, name: Tianfan Xue

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/.