Our final goal is grasping the object in underwater based on the RGB camera installed in AUV. In this thesis, we made the base framework for grasping the object. So we propose the method of improving underwater object detection and pose estimation sequentially.
Firstly, underwater images are affected by the various optical variation such as color distortion, intensity degeneration, haze, and so on. So to recognize the object in underwater images, we have to add additional process to remove the optical variation for the accurate object detection. Especially, in deep learning-based object detection model, the training set applied above process is the most effective to obtain the outstanding performance. In this thesis, we propose the novel method of generating the underwater dataset. This dataset reflects the various optical conditions which are color distortion, intensity degeneration, haze effect. Also, the object occlusion is included in our dataset generation process. In the experiment, we evaluate the suitability of our dataset for the underwater environment to determine if our dataset reflects the underwater environment.
As the next step, we introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. Our approach initially trains a Variational AutoEncoder (VAE) to learn the code for each object, which is then further refined by a novel rotational primitive decoder. Doing so substantially improves the orientation estimation in a direct regression fashion as well as overall pose estimation performance. To better capture the representation of the learned code, we concatenate the sampled codes prior to the orientation estimation. Lastly, translation is inferred using an object relocalization module. Because of the enhanced rotational discriminative code, high accuracy is achieved for symmetric and occluded objects. In addition, to make a more accurate pose estimation result, we propose RGB-based pose refinement network.
본학위논문은카메라센서를이용하여수중환경의물체를인식과그물체의자세를추정하는방법에 대해제안한다. 수중환경에서촬영된이미지는색상왜곡, 빛의감쇠등과같은다양한광학적현상의영향 으로 인해 물체 인식 알고리즘에 사용하기 위해서는 별도의 추가적 처리가 필수적이다. 본 논문에서는 수중 환경을 반영하기 위한 추가적 처리를 거친 합성 이미지 데이터 제작 방법을 제안하고, 이를 딥러닝을 활용한 물체 인식 알고리즘에 적용한다. 제작된 데이터 셋을 이용하여 학습된 물체 인식 알고리즘은 수중 물체 인식에 강인할 수 있음을 실험을 통해 보인다. 추가적으로 본 논문에서는 로테이션 프리미티브를 적극 활용한 새로운 물체 자세 추정 방법을 제안한다. 제안한 물체 자세 추정 방법을 다양한 데이터셋에서의 실험을 통해 강인한 물체자세추정을할수있음을보인다. 이를통해수중환경에서수중로봇이물체를집는작업을하기위한 토대를 마련한다.
