3,475 research outputs found
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Recent robotic manipulation competitions have highlighted that sophisticated
robots still struggle to achieve fast and reliable perception of task-relevant
objects in complex, realistic scenarios. To improve these systems' perceptive
speed and robustness, we present SegICP, a novel integrated solution to object
recognition and pose estimation. SegICP couples convolutional neural networks
and multi-hypothesis point cloud registration to achieve both robust pixel-wise
semantic segmentation as well as accurate and real-time 6-DOF pose estimation
for relevant objects. Our architecture achieves 1cm position error and
<5^\circ$ angle error in real time without an initial seed. We evaluate and
benchmark SegICP against an annotated dataset generated by motion capture.Comment: IROS camera-read
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
This paper presents an end-to-end convolutional neural network (CNN) for
2D-3D exemplar detection. We demonstrate that the ability to adapt the features
of natural images to better align with those of CAD rendered views is critical
to the success of our technique. We show that the adaptation can be learned by
compositing rendered views of textured object models on natural images. Our
approach can be naturally incorporated into a CNN detection pipeline and
extends the accuracy and speed benefits from recent advances in deep learning
to 2D-3D exemplar detection. We applied our method to two tasks: instance
detection, where we evaluated on the IKEA dataset, and object category
detection, where we out-perform Aubry et al. for "chair" detection on a subset
of the Pascal VOC dataset.Comment: To appear in CVPR 201
Towards binocular active vision in a robot head system
This paper presents the first results of an investigation and pilot study into an active, binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognizing objects in a highly-cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the âsearchlight metaphorâ of biological systems. We present results of a first pilot investigation that yield a maximum vergence error of 6.4 pixels, while seven of nine known objects were recognized in a high-cluttered environment. Finally a âstepping stoneâ visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the Field of View resulting from any individual saccade
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