SuperEx：利用非视距感知增强室内建图与探索
SuperEx: Enhancing Indoor Mapping and Exploration using Non-Line-of-Sight Perception
AI总结：
本文提出 SuperEx 框架，将非视距（NLOS）单光子 LiDAR 感知集成到机器人建图与探索循环中，通过物理与数据驱动方法重建隐藏结构，显著提升了低覆盖率下的建图精度与探索效率。
摘要：
在未知室内环境中进行高效探索与建图是一项基础性挑战，在时间紧迫的场景中风险极高。在当前系统中，机器人感知仍局限于视距范围内；被遮挡的区域在物理穿越之前仍处于未知状态，当布局偏离先验假设时会导致探索效率低下。在本研究中，我们将非视距（NLOS）感知引入机器人探索。我们利用单光子 LiDAR，其捕获的飞行时间直方图编码了隐藏物体的存在——使机器人能够看到盲角。近期的单光子 LiDAR 已变得实用且便携，能够在受控实验室环境之外进行部署。先前的 NLOS 研究主要针对静态、实验室场景中的 3D 重建，而 NLOS 辅助导航的初步尝试仅考虑简化的几何结构。我们引入了 SuperEx，这是一个将 NLOS 感知直接集成到建图-探索循环中的框架。SuperEx 通过以下方式利用超视距线索增强全局地图预测：从时间直方图中雕刻出空的 NLOS 区域；以及通过利用结构规律性的两步基于物理与数据驱动的方法重建占据结构。在复杂模拟地图和真实世界 KTH Floorplan 数据集上的评估表明，在覆盖率低于 30% 的情况下，建图准确率提升了 12%，并且与视距基线相比提高了探索效率，为实现超越直接可见范围的可靠建图开辟了道路。
英文摘要：
Efficient exploration and mapping in unknown indoor environments is a fundamental challenge, with high stakes in time-critical settings. In current systems, robot perception remains confined to line-of-sight; occluded regions remain unknown until physically traversed, leading to inefficient exploration when layouts deviate from prior assumptions. In this work, we bring non-line-of-sight (NLOS) sensing to robotic exploration. We leverage single-photon LiDARs, which capture time-of-flight histograms that encode the presence of hidden objects - allowing robots to look around blind corners. Recent single-photon LiDARs have become practical and portable, enabling deployment beyond controlled lab settings. Prior NLOS works target 3D reconstruction in static, lab-based scenarios, and initial efforts toward NLOS-aided navigation consider simplified geometries. We introduce SuperEx, a framework that integrates NLOS sensing directly into the mapping-exploration loop. SuperEx augments global map prediction with beyond-line-of-sight cues by (i) carving empty NLOS regions from timing histograms and (ii) reconstructing occupied structure via a two-step physics-based and data-driven approach that leverages structural regularities. Evaluations on complex simulated maps and the real-world KTH Floorplan dataset show a 12% gain in mapping accuracy under < 30% coverage and improved exploration efficiency compared to line-of-sight baselines, opening a path to reliable mapping beyond direct visibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
