オクルージョンと高速物体運動下での自己回復機能を備えた頑健な6-DoF物体姿勢追跡
Robust 6-DoF Object Pose Tracking with Built-In Recovery under Occlusions and Rapid Object Motions
RGB-Dデータを用いた未学習物体の6-DoF姿勢追跡において、学習ベースのキーポイントマッチングと最適化ベースの位置合わせを組み合わせ、追跡失敗の検出と自動回復を行うモジュールを新たに提案した。オクルージョンや高速運動下でも頑健に追跡を継続できる。
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著者: Balázs Opra, Léo Ghafari, Thomas Stewart, Cyrill Stachniss
分類: cs.CV
原文アブストラクト
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.
