论文标题
对称和不确定性感知的对象大满贯6DOF对象姿势估计
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation
论文作者
论文摘要
我们提出了一个基于关键点的对象级别的SLAM框架，该框架可以为对称对象和非对称对象提供全球一致的6DOF姿势估计。据我们所知，我们的系统是最早利用SLAM的相机姿势信息的系统之一，以提供先验知识以跟踪对称对象的关键点 - 确保新的测量与当前的3D场景一致。此外，我们的语义关键点网络经过训练，可以预测捕获预测真正误差的关键点的高斯协方差，因此，不仅可以作为系统优化问题中残差的重量有用，还可以作为检测有害统计异常的手段而不选择手动阈值。实验表明，我们的方法以6DOF对象的姿势估算和实时速度为最先进的状态提供了竞争性能。我们的代码，预培训模型和关键点标签可用https://github.com/rpng/suo_slam。
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.
