PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
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Estimating the 6D pose of known objects is important for robots to interact with objects in the real world. The problem is challenging due to the variety of objects as well as the complexity of the scene caused by clutter and occlusion between objects. In this work, we introduce a new Convolutional Neural Network (CNN) for 6D object pose estimation named PoseCNN. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. PoseCNN is able to handle symmetric objects and is also robust to occlusion between objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN provides very good estimates using only color as input.
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