Location: Search Based Planning Lab, RI, CMU

Learning based discriminative 6-Dof pose estimation methods require large datasets with annotated ground truth poses. Deliberative search based perception methods like PERCH 2.0 make assumptions that limit their scalibility in 6-Dof pose estimation scenarios. In this work, we combine the two together, building on their strengths and addressing the shortcomings of each. Our MaskRCNN and PERCH 2.0 based framework achieves better accuracy than state-of-the-art purely discriminative methods and requires training only for instance segmentation.

Paper : Master’s Thesis, IROS 2020 / Videos : Clip 1 / Code : GitHub

Skills : C++, CUDA, Python, ROS, PCL, OpenCV, OpenGL, MPI, PyTorch, Unity