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We propose a keypoint-based object-level SLAM framework that can provideglobally consistent 6DoF pose estimates for symmetric and asymmetric objectsalike. To the best of our knowledge, our system is among the first to utilizethe camera pose information from SLAM to provide prior knowledge for trackingkeypoints on symmetric objects -- ensuring that new measurements are consistentwith the current 3D scene. Moreover, our semantic keypoint network is trainedto predict the Gaussian covariance for the keypoints that captures the trueerror of the prediction, and thus is not only useful as a weight for theresiduals in the system's optimization problems, but also as a means to detectharmful statistical outliers without choosing a manual threshold. Experimentsshow that our method provides competitive performance to the state of the artin 6DoF object pose estimation, and at a real-time speed. Our code, pre-trainedmodels, and keypoint labels are available https://github.com/rpng/suo_slam.

Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation