Toronto Robotics and Artificial Intelligence Laboratory’s Post

6D pose estimation of textureless shiny objects has become an essential problem in many robotic applications. Many pose estimators require high-quality depth data, often measured by structured light cameras. However, when objects have shiny surfaces (e.g., metal parts), these cameras fail to sense complete depths from a single viewpoint due to the specular reflection, resulting in a significant drop in the final pose accuracy. We are thrilled to share that our latest research has been accepted to #IROS2024! 🎉 In our paper, "Active Pose Refinement for Textureless Shiny Objects using the Structured Light Camera," co-authored by Yang J., Jian Yao, and Steven Lake Waslander, we present a complete active vision framework for 6D object pose refinement and next-best view prediction to mitigate the aforementioned issue. 🌟 Key Highlights 🌟: - Innovative 6D Pose Refinement: Our approach is tailored for SLI cameras and includes estimating pixel depth uncertainties and integrating these estimates into our SDF-based pose refinement module. - Surface Reflection Model: We predict depth uncertainties for unseen viewpoints using a reflection model that recovers object reflection parameters with a differentiable renderer. - Active Vision System: By integrating our reflection model and pose refinement approach, we can predict the next-best view (NBV) for pose estimation through online rendering. Check out the full paper linked below and join us at #IROS2024! Paper: https://lnkd.in/gMTHZVcY A big thank you to Epson Canada for supporting this work! #poseestimation #robotics #sli #structuredlightcamera

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