  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.
