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Radar SLAM can now achieve state-of-the-art performance via direct scan registration, eliminating the need for hand-engineered feature extraction and enabling robust localization in adverse weather.
LiDAR odometry can be made significantly more robust in challenging, feature-poor environments by representing surfaces as compact voxel-wise oriented height images and focusing registration on geometrically informative regions.
Robots can now adapt demonstrated skills to significantly different starting conditions thanks to a Gaussian Process representation that preserves kinematic profiles.
