Self-Supervised Doppler-Guided RF Odometry

Accurate RF odometry remains difficult due to multipath interference, geometric degeneracy, and dynamic clutter. Existing approaches mitigate these challenges by relying on auxiliary sensors or ground-truth pose supervision, which limits scalability and deployability. We present RFWalk, a self-supervised, RF-only odometry framework that learns cross-frame correspondence without any pose annotations or auxiliary sensors. Our key insight is to cast each range-azimuth cell as a node in a space-time graph and recover inter-frame motion by learning a soft transition matrix through cycle-consistent contrastive random walks. To resolve geometric degeneracy in feature-poor scenes, we further introduce a physics-informed Doppler consistency loss that enforces agreement between the correspondence-induced displacement field and measured radial velocities. Experiments on challenging scenes show that RFWalk achieves competitive accuracy in static environments and substantially outperforms existing approaches in dynamic scenes.