提出统一表示法，让机器人高效感知近距与远距语义信息。
RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration
- 用体素和射线联合编码近远距离语义，支持开放集映射。
- 零样本3D语义分割性能提升1.34倍，吞吐量提高16.5倍。
- 适用于实时导航与探索，适合高动态开放世界任务。
开放集语义映射对开放世界的机器人至关重要。现有方法或受限于深度范围，或仅在受限环境下映射远距离物体，难以融合近距与远距观测，且在细粒度语义与效率间存在权衡。本文提出 RayFronts，一种统一表示，实现密集与远距离的高效语义建图。该方法将任务无关的开放集语义编码至近距体素与远距边界射线中，显著减少搜索空间，使机器人在感知范围内与范围外均能做出明智决策，且在 Orin AGX 上运行速度达 8.84 Hz。基准测试显示，其近距语义表现优于基线：零样本3D语义分割性能提升1.34倍，吞吐量提高16.5倍。传统在线建图评估常受其他系统组件干扰，本文提出无规划器依赖的评估框架，有效衡量远距搜索与探索能力，结果显示，RayFronts 比最接近的在线基线减少搜索体积2.2倍。
原文摘要 · Abstract (English)
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.
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