发表机构
Woven by Toyota, Inc.; University of Bonn; University of Bonn, Center for Robotics, and the Lamarr Institute for Machine Learning and Artificial Intelligence（丰田编织公司; 波恩大学; 波恩大学机器人中心以及拉玛尔机器学习与人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RGB-D数据中未见物体的6自由度跟踪问题，尤其是遮挡和快速运动场景，提出结合关键点匹配与优化对齐及故障检测恢复模块的方法，在多场景评估中表现出色，是鲁棒6自由度物体跟踪的重要进展。
AI 中文摘要
实时6自由度物体姿态跟踪对许多机器人应用至关重要，现有多种方法，但在临时完全遮挡和快速物体运动下仍不可靠，跟踪丢失后大多需手动重新初始化。本文针对从RGB-D数据中对未见物体进行基于模型的鲁棒6自由度跟踪问题，尤其是在有遮挡和快速运动的场景中。提出一种结合基于学习的高效关键点匹配和基于优化的对齐的新方法，并引入新的故障检测和恢复模块。系统监测姿态可靠性，检测跟踪分歧或遮挡，执行全局重新检测和姿态估计步骤，在恢复跟踪前稳健验证恢复候选。在标准跟踪基准和新的遮挡与快速运动场景数据集上的评估表明，该方法在简单跟踪序列上匹配最先进精度，保持57.6帧每秒的高跟踪速度，在具有挑战性条件下提供最稳健跟踪性能。因此，该方法是从RGB-D数据进行鲁棒6自由度物体跟踪的重要进展。
英文摘要
Real-time 6-DoF object pose tracking is essential for many robotics applications, and several approaches exist. Yet even today's approaches remain unreliable under temporary full occlusions and rapid object motions. Once tracking is lost, most methods struggle to detect the failure and recover automatically, often requiring manual re-initialization. In this paper, we address the problem of robust model-based 6-DoF tracking of unseen objects from RGB-D data, especially in scenarios with occlusion and fast motion. We propose a novel method that combines efficient learning-based keypoint matching with optimization-based alignment and introduces a novel failure detection and recovery module. Our system monitors pose reliability, detects tracking divergence or occlusions, and performs a global re-detection and pose estimation step that robustly verifies recovery candidates before resuming tracking. Our evaluation on standard tracking benchmarks and on a new dataset of occluded and fast-moving scenes shows that our method matches state-of-the-art accuracy on easy tracking sequences, maintains high tracking speed at 57.6 frames per second, and provides the most robust tracking performance under challenging conditions. Thus, we believe that our approach is a relevant step forward in robust 6-DoF object tracking from RGB-D data.
Comments8 pages, 4 figures. Accepted for publication in IEEE Robotics and Automation Letters (RA-L)
