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A novel lightweight network achieves significant accuracy improvements in human pose estimation from planar LiDAR, making it feasible for real-time applications on resource-constrained robots.
Nano-drones can now visually estimate gate poses with significantly improved accuracy in the real world, thanks to a self-supervised domain adaptation technique that leverages onboard odometry.