发表机构
Robotics Research Center, IIIT-Hyderabad（机器人研究中心，印度国际信息技术学院海得拉巴分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对传统3D环境自主探索方法忽视结构背景致轨迹低效及重建质量差的问题，提出SCAGE框架，将探索视为几何异常最小化问题，依据对室内建筑的理解标记异常引导机器人，实现空间发现与高保真映射结合，提升了覆盖率和重建质量。
AI 中文摘要
传统上，未知3D环境的自主探索由最大化覆盖的几何启发式方法驱动。但这些方法确定探索目标时未考虑潜在结构背景，导致轨迹效率低，限制最终3D重建的保真度。为弥合空间覆盖与重建质量间的差距，我们引入新范式：将探索重新定义为几何异常最小化问题。提出SCAGE框架，它直接在非结构化3D点云上运行。机器人依据对标准室内建筑的理解评估实时3D观测，将与先验不符的区域标记为场景异常，以此引导机器人从最佳视角调查并解决结构异常。该方法将空间发现与高保真映射无缝结合。大量评估表明，与现有基线相比，SCAGE实现了更高的体积覆盖率（所有场景约90%）和更高的3D重建质量。
英文摘要
Autonomous exploration of unknown 3D environments is traditionally driven by coverage-maximizing geometric heuristics. However, these methods typically determine exploration targets without considering the underlying structural context. This leads to inefficient trajectories often limiting the fidelity of the final 3D reconstruction. To bridge the gap between spatial coverage and reconstruction quality, we introduce a novel paradigm: reframing exploration as a geometric anomaly minimization problem. We present SCAGE: SCene Anomaly Guided Exploration, a novel autonomous exploration framework that operates directly on unstructured 3D point clouds. Instead of blindly chasing volumetric boundaries, we equip the robot with a foundational understanding of standard indoor architecture. As the robot navigates, it continuously evaluates its live 3D observations against these learned expectations. When the incoming geometry contradicts the learned priors of a typical indoor environment, such as a fragmented wall or a partial table, the system flags these regions as scene anomalies. These geometric inconsistencies act as a guiding signal, naturally drawing the robot to investigate and resolve these structural anomalies from optimal vantage points. By actively targeting poorly reconstructed regions rather than just empty space, our approach seamlessly couples spatial discovery with high-fidelity mapping. Extensive evaluations demonstrate that SCAGE achieves superior volumetric coverage (~90% in all scenes) and higher 3D reconstruction quality compared to state-of-the-art baselines.
CommentsAccepted in IEEE/RSJ IROS 2026. Project page: https://beyondfrontiers.github.io/
