RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration

Open-set semantic mapping is crucial for open-world robots. Current mapping approaches either are limited by the depth range or only map beyond-range entities in constrained settings, where overall they fail to combine within-range and beyond-range observations. Furthermore, these methods make a trade-off between fine-grained semantics and efficiency. We introduce RayFronts, a unified representation that enables both dense and beyond-range efficient semantic mapping. RayFronts encodes task-agnostic open-set semantics to both in-range voxels and beyond-range rays encoded at map boundaries, empowering the robot to reduce search volumes significantly and make informed decisions both within & beyond sensory range, while running at 8.84 Hz on an Orin AGX. Benchmarking the within-range semantics shows that RayFronts's fine-grained image encoding provides 1.34x zero-shot 3D semantic segmentation performance while improving throughput by 16.5x. Traditionally, online mapping performance is entangled with other system components, complicating evaluation. We propose a planner-agnostic evaluation framework that captures the utility for online beyond-range search and exploration, and show RayFronts reduces search volume 2.2x more efficiently than the closest online baselines.

@article{alama2025_2504.06994,
  title={ RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration },
  author={ Omar Alama and Avigyan Bhattacharya and Haoyang He and Seungchan Kim and Yuheng Qiu and Wenshan Wang and Cherie Ho and Nikhil Keetha and Sebastian Scherer },
  journal={arXiv preprint arXiv:2504.06994},
  year={ 2025 }
}