Aug 2026· Robotics· Vol 15, pp. 157· 0 citations· 55 references
TL;DR
This work proposes an improved geometric hashing method that robustly estimates the rigid transformation in the presence of noise and outliers, and demonstrates its effectiveness and speed using both simulated and real-world datasets.
Abstract
Accurately measuring the six degrees of freedom (6-DoF) pose of a sample is a critical prerequisite for applications in robotic sample handling, optical metrology, and industrial quality control. Our pose estimation problem requires matching a small, partial observation of 4–20 discrete 3D points to a reference point set of up to 50 points. Popular registration algorithms such as Go-ICP, TEASER++, and MAC are designed for large-scale correspondence problems and do not perform reliably in our operating regime. Therefore, we propose an improved geometric hashing method that robustly estimates the rigid transformation in the presence of noise and outliers, and demonstrate its effectiveness and speed using both simulated and real-world datasets.
A two-stage method for accurate 6D pose estimation using consumer-grade depth cameras in real-world scenarios with YOLO-based detection, Euclidean clustering, and moving least squares smoothing combined to extract high-quality target point clouds from noisy RGB-D observations is proposed.
PURPOSE
Accurate 6 DoF pose estimation of surgical tools is critical for automation, robotic proprioception, and safe interaction with the tissue operated on. Kinematics-based approaches suffer from accumulated errors due to the cable-driven nature of robotic arms, while vision-based methods often rely on external mark...
Lucy Fothergill, Pietro Valdastri, Dominic Jones et al.· International Journal of Com...· 0 citations
Precise 6D pose estimation of non-cooperative space targets is a core enabling technology for on-orbit servicing. Existing methods largely rely on predefined CAD models, which often suffer significant performance degradation when deployed in space environments characterized by extreme depth variations. In this paper, a...
Han-Jian Xu, Li-Min Xu, Zhi-Ji Zhu et al.· International Conference on...· 0 citations
This paper studies monocular 6D pose estimation of small cubic objects from a single RGB image and proposes a two-stage manipulation- oriented framework, which achieves the strongest overall balance in ADD-S, translation accuracy, rotation stability, and task-oriented usability metrics.
Xinmiao Du· Poster Volume 0007 The 2026...· 0 citations
Detecting and grasping small objects remains a significant challenge in robotics. Active vision, where the robot moves closer to the object, is an intuitive solution, yet comparing approaches on common ground is difficult since identical physical scene setups are required. Hence, we introduce BRAVE-6D, a benchmark desi...
Philipp Ausserlechner, Bernhard Neuberger, Alessandro Scherl et al.· 0 citations
This paper proposes a 3D point tracker accurate in those absolute terms and operating within a single commodity GPU, pose-free, monocular budget, exceeding strong feed-forward trackers and a companion analysis explains why several published trackers lose most of their accuracy under this budget.
Masahiro Ogawa, Qi An, Atsushi Yamashita· 0 citations
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