Data-Association-Free Landmark-based SLAM

We study landmark-based SLAM with unknown data association: our robot navigates in a completely unknown environment and has to simultaneously reason over its own trajectory, the positions of an unknown number of landmarks in the environment, and potential data associations between measurements and landmarks. This setup is interesting since: (i) it arises when recovering from data association failures or from SLAM with information-poor sensors, (ii) it sheds light on fundamental limits (and hardness) of landmark-based SLAM problems irrespective of the front-end data association method, and (iii) it generalizes existing approaches where data association is assumed to be known or partially known. We approach the problem by splitting it into an inner problem of estimating the trajectory, landmark positions and data associations and an outer problem of estimating the number of landmarks. Our approach creates useful and novel connections with existing techniques from discrete-continuous optimization (e.g., k-means clustering), which has the potential to trigger novel research. We demonstrate the proposed approaches in extensive simulations and on real datasets and show that the proposed techniques outperform typical data association baselines and are even competitive against an "oracle" baseline which has access to the number of landmarks and an initial guess for each landmark.

SLAM-Supported Self-Training for 6D Object Pose Estimation

Recent progress in learning-based object pose estimation paves the way for developing richer object-level world representations. However, the estimators, often trained with out-of-domain data, can suffer performance degradation as deployed in novel environments. To address the problem, we present a SLAM-supported self-training procedure to autonomously improve robot object pose estimation ability during navigation. Combining the network predictions with robot odometry, we can build a consistent object-level environment map via pose graph optimization (PGO). Exploiting the state estimates from PGO, we pseudo-label robot-collected RGB images to fine-tune the pose estimators. Unfortunately, it is difficult to model the uncertainty of the estimator predictions. The unmodeled uncertainty in the data used for PGO can result in low-quality object pose estimates. An automatic covariance tuning method is developed for robust PGO by allowing the measurement uncertainty models to change as part of the optimization process. The formulation permits a straightforward alternating minimization procedure that re-scales covariances analytically and component-wise, enabling more flexible noise modeling for learning-based measurements. We test our method with the deep object pose estimator (DOPE) on the YCB video dataset and in real-world robot experiments. The method can achieve significant performance gain in pose estimation, and in return facilitates the success of object SLAM.