69 research outputs found
Segmentation-driven 6D Object Pose Estimation
The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. In both cases, the object is treated as a global entity, and a single pose estimate is computed. As a consequence, the resulting techniques can be vulnerable to large occlusions. In this paper, we introduce a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations. We then use a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences, from which a reliable pose estimate can be obtained. We outperform the state-of-the-art on the challenging Occluded-LINEMOD and YCB-Video datasets, which is evidence that our approach deals well with multiple poorly-textured objects occluding each other. Furthermore, it relies on a simple enough architecture to achieve real-time performance
MBAPose: Mask and Bounding-Box Aware Pose Estimation of Surgical Instruments with Photorealistic Domain Randomization
Surgical robots are controlled using a priori models based on robots'
geometric parameters, which are calibrated before the surgical procedure. One
of the challenges in using robots in real surgical settings is that parameters
change over time, consequently deteriorating control accuracy. In this context,
our group has been investigating online calibration strategies without added
sensors. In one step toward that goal, we have developed an algorithm to
estimate the pose of the instruments' shafts in endoscopic images. In this
study, we build upon that earlier work and propose a new framework to more
precisely estimate the pose of a rigid surgical instrument. Our strategy is
based on a novel pose estimation model called MBAPose and the use of synthetic
training data. Our experiments demonstrated an improvement of 21 % for
translation error and 26 % for orientation error on synthetic test data with
respect to our previous work. Results with real test data provide a baseline
for further research.Comment: 8 pages, submitted to IROS202
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