自适应深度图引导的捆绑调整用于无对应关系的多视图点云配准
Adaptive Depth-Map-Guided Bundle Adjustment for Correspondence-Free Multi-View Point Cloud Registration
- Robotics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院机器人研究所）
- School of Informatics, The University of Edinburgh（爱丁堡大学信息学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对工业废钢处理的多视图点云配准难题，提出自适应分层深度图引导的无对应关系捆绑调整框架，在保持低计算成本的同时实现高精度鲁棒重建
AI中文摘要：
不规则废钢的机器人处理需要密集的三维测量，以替代危险切割工作单元中的人工视觉评估。重建的地图用于估算工件尺寸、边界几何形状、可行的预热和切割区域，以及考虑碰撞的焊炬路径。因此，重建误差会直接传播到下游的测量和规划环节。现有的多视图配准方法通常依赖特征提取和数据关联来建立视图之间的对应关系。然而，在具有光滑金属表面、重复结构、遮挡和部分重叠的工作单元中，可能会建立错误的对应关系，导致位姿估计不准确和重建失真。本文提出了一种自适应分层深度图引导的捆绑调整框架，用于无对应关系的多视图点云配准。该场景由全局二维半网格表示，每个单元可自适应地维持多个深度假设。原始深度观测值被直接投影到全局地图中，以形成深度约束，无需显式的特征对应关系。在多个表面产生冲突深度的网格单元处，基于softmax的层分配将每个观测值链接到兼容的深度假设。所得的非线性最小二乘公式联合优化传感器位姿和分层深度图，对应关系由深度图表示和投影模型隐式诱导。在自行收集的工业数据集上进行的实验表明，所提出的方法在具有挑战性的工业场景中，在保持竞争力的重建精度的同时，还具备鲁棒性和低计算成本。我们在以下网址发布了开源代码实现：this https URL
英文摘要：
Robotic processing of irregular steel scrap requires dense 3-D measurement to replace manual visual assessment in hazardous cutting workcells. The reconstructed map is used to estimate piece dimensions, boundary geometry, feasible preheating and cutting regions, and collision-aware torch paths. The reconstruction errors therefore propagate directly to downstream measurement and planning. Existing multi-view registration methods commonly rely on feature extraction and data association to establish correspondences between views. In workcells with smooth metallic surfaces, repeated structures, occlusions, and partial overlaps, however, wrong correspondences may be established, leading to inaccurate pose estimation and distorted reconstruction. This paper presents an adaptive layered depth-map-guided bundle adjustment framework for correspondence-free multi-view point cloud registration. The scene is represented by a global 2.5-D grid, where each cell can adaptively maintain multiple depth hypotheses. Raw depth observations are directly projected into the global map to form depth constraints without explicit feature correspondences. At grid cells where multiple surfaces produce conflicting depths, a softmax-based layer assignment links each observation to compatible depth hypotheses. The resulting nonlinear least-squares formulation jointly refines sensor poses and the layered depth map, with correspondences implicitly induced by the depth-map representation and projection model. Experiments on self-collected industrial datasets show that the proposed method achieves consistently competitive reconstruction accuracy while maintaining robustness and low computational cost in challenging industrial scenarios. We release the open-source code implementation at: https://github.com/YiranZhou-Robotics/ADM-BA.git
