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Scalix achieves state-of-the-art scale consistency in monocular SLAM by treating scale predictions as independent measurements, revolutionizing how robots perceive their environments.
Surprisingly, fusing a 2D imaging radar with a gyroscope achieves lidar-level odometry accuracy, unlocking a potentially cheaper and more robust alternative to lidar-based navigation.
Robots can now adapt demonstrated skills to significantly different starting conditions thanks to a Gaussian Process representation that preserves kinematic profiles.
State-of-the-art autonomous driving algorithms falter on the new Boreas Road Trip dataset, revealing their tendency to overfit to simpler driving environments.
