GLEAM: Learning Generalizable Exploration Policy for Active Mapping in
  Complex 3D Indoor Scenes
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/.
683552b7d34b8e5da4d9dfe3
Xiao Chen
claimed_verified
2025-05-27T07:49:12.484Z
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Xiao Chen
user
Xiao-HF
683552b7d34b8e5da4d9dfe4
Tai Wang
admin_assigned
2025-05-27T12:47:38.917Z
64e6d9d229a548f66aff6e5b
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Tai Wang
user
taiwang
683552b7d34b8e5da4d9dfe5
Quanyi Li
admin_assigned
2025-05-27T12:47:23.428Z
679891b44a7043fef939ebf9
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Quanyi Li
user
kok123
683552b7d34b8e5da4d9dfe6
Tao Huang
683552b7d34b8e5da4d9dfe7
Jiangmiao Pang
admin_assigned
2025-05-27T12:47:14.318Z
65783ee6ee33d547aecc3ffc
https://cdn-avatars.huggingface.co/v1/production/uploads/65783ee6ee33d547aecc3ffc/lWZX88c-0dCsN-yB9Jhlf.jpeg
Jiangmiao Pang
user
Jiangmiao
683552b7d34b8e5da4d9dfe8
Tianfan Xue
admin_assigned
2025-05-27T12:47:08.189Z
67631f57abfbd60470d4b3c3
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/5VrG0IZjYrDLiatOr4y06.png
Tianfan Xue
user
littlemouse9
GLEAM: Learning Generalizable Exploration Policy for Active Mapping in
  Complex 3D Indoor Scenes
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/.
A new benchmark and policy, GLEAM-Bench and GLEAM, improve the scalability and reliability of active mapping in complex environments through semantic representations and efficient exploration strategies.
active mapping generalizable exploration semantic representations navigable goals randomized strategies 3D scenes synthetic datasets real-scan datasets benchmark mapping accuracy coverage trajectories