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L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation with Small Datasets

Mathieu Gonzalez 1 Amine Kacete 1 Albert Murienne 1 Eric Marchand 2
2 RAINBOW - Sensor-based and interactive robotics
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Estimating the 3D pose of an object is a challenging task that can be considered within augmented reality or robotic applications. In this paper, we propose a novel approach to perform 6 DoF object pose estimation from a single RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and geometric respectively. The data-driven step consists of a classification CNN to estimate the object 2D location in the image from local patches, followed by a regression CNN trained to predict the 3D location of a set of keypoints in the camera coordinate system. To extract the pose information, the geometric step consists in aligning the 3D points in the camera coordinate system with the corresponding 3D points in world coordinate system by minimizing a registration error, thus computing the pose. Our experiments on the standard dataset LineMod show that our approach is more robust and accurate than state-of-theart methods. The approach is also validated to achieve a 6 DoF positioning task by visual servoing.
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https://hal.inria.fr/hal-03148929
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Submitted on : Monday, February 22, 2021 - 4:08:39 PM
Last modification on : Tuesday, October 19, 2021 - 11:04:42 AM
Long-term archiving on: : Sunday, May 23, 2021 - 7:01:28 PM

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Mathieu Gonzalez, Amine Kacete, Albert Murienne, Eric Marchand. L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation with Small Datasets. IEEE Robotics and Automation Letters, IEEE 2021, 6 (2), pp.2914-2921. ⟨10.1109/LRA.2021.3062605⟩. ⟨hal-03148929⟩

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