対称性と不確かさを考慮した物体SLAMによる6DoF物体姿勢推定
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation
SLAMのカメラ姿勢を利用して対称物体のキーポイント追跡を安定化し、キーポイントの不確かさを学習して外れ値除去と最適化に活用する物体レベルSLAMを提案した論文。
著者: Nathaniel Merrill, Yuliang Guo, Xingxing Zuo, Xinyu Huang, Stefan Leutenegger, Xi Peng, Liu Ren, Guoquan Huang
分類: cs.RO
原文アブストラクト
We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our knowledge, our system is among the first to utilize the camera pose information from SLAM to provide prior knowledge for tracking keypoints on symmetric objects -- ensuring that new measurements are consistent with the current 3D scene. Moreover, our semantic keypoint network is trained to predict the Gaussian covariance for the keypoints that captures the true error of the prediction, and thus is not only useful as a weight for the residuals in the system's optimization problems, but also as a means to detect harmful statistical outliers without choosing a manual threshold. Experiments show that our method provides competitive performance to the state of the art in 6DoF object pose estimation, and at a real-time speed. Our code, pre-trained models, and keypoint labels are available https://github.com/rpng/suo_slam.
