发表机构
University of Canterbury（坎特伯雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种基于稀疏激光雷达的实时表面可访问性场方法，无需先验场景模型，在工作站和Jetson Orin上运行，F1值达90.8，可准确识别不可访问表面，弥补可见性估计的不足。
AI 中文摘要
理解场景中哪些表面可供给定工具物理访问是机器人交互的基础，但3D感知系统通常仅停留在几何重建或可见性估计层面。现有几何可访问性方法需要完整、无噪声的网格和固定运动学基，这些假设对于从实时数据逐步建图的移动平台而言不成立；可见性估计无法考虑工具几何形状或接近通道的净空。我们提出可访问性场：针对给定工具的逐点表面可访问性标注，从流式稀疏激光雷达实时生成，并随平台移动以传感器速率更新。我们的方法完全在GPU上运行，针对代表工具在一组旋转接近姿态下的预计算几何内核评估每个表面点，检查工具碰撞和接近通道净空。以扫描为中心的截断符号距离场集成方案支撑我们的系统，仅更新每个观测回波附近的体素，而非每帧投影所有视锥体体素——这对Livox Mid-360等非重复传感器至关重要，这类传感器的某些bin无回波。我们的系统与工具无关，无需先验场景模型，可在工作站和Jetson Orin边缘硬件上运行。我们在合成物体和成熟规模的Pinus radiata模型上进行定量评估，显示仅可见性不足以作为可访问性代理：在混合可访问性几何上，我们的方法F1值为90.8，而Hidden Point Removal基线为69.8，且尽管从传感器可见，仍正确识别56.8%的松树枝表面为不可访问。据我们所知，这是首个无需先验场景模型或固定基帧，从流式稀疏激光雷达实时估计逐点表面可访问性的方法——这是可见性估计无法提供的能力。
英文摘要
Understanding which surfaces in a scene are physically accessible to a given tool is fundamental for robotic interaction, yet 3D perception systems typically stop at geometric reconstruction or visibility estimation. Existing geometric accessibility methods require complete, noise-free meshes and fixed kinematic bases, assumptions that fail for mobile platforms mapping incrementally from live data; visibility estimation cannot account for tool geometry or approach-corridor clearance. We propose the Accessibility Field: a per-point labelling of surface accessibility for a given tool, produced in real time from streaming sparse LiDAR and updated at sensor rate as the platform moves. Running entirely on GPU, our method evaluates each surface point against precomputed geometry kernels representing the tool at a set of rotated approach orientations, checking tool collisions and approach-corridor clearance. A scan-centric Truncated Signed Distance Field integration scheme underpins our system, updating only voxels near each observed return rather than projecting every frustum voxel each frame -- critical for nonrepetitive sensors like the Livox Mid-360, where some bins contain no returns. Our system is tool-agnostic, needs no prior scene model, and runs on workstation and Jetson Orin edge hardware. We evaluate quantitatively on synthetic objects and mature-scale Pinus radiata models, showing visibility alone is insufficient as an accessibility proxy: our method achieves F1=90.8 vs. 69.8 for a Hidden Point Removal baseline on mixed-accessibility geometry, and correctly identifies 56.8% of pine branch surfaces as inaccessible despite being visible from the sensor. To our knowledge, this is the first method to estimate per-point surface accessibility in real time from streaming sparse LiDAR without a prior scene model or fixed base frame -- a capability visibility estimation cannot provide.
