Enabling Autonomous Navigation With Radar-Only Perception in GPS-Denied Environments

Autonomous UAVs operating in GPS-denied environments rely heavily on onboard perception for safe navigation, yet traditional LiDAR and camera-based systems falter in poor lighting and weather situations. In contrast, millimetre wave (mmWave) radar offers superior robustness and low power consumption but produces extremely sparse, noisy, and low-resolution measurements that limit its utility for dense 3D understanding. This work proposes a novel radar-only inference framework that reconstructs dense LiDAR-like point clouds by leveraging a stereo millimeter-wave radar (IWR6843AoP) data with hallucinated Time-of-Flight (ToF) representations. Rather than relying on explicit ToF sensors, our method learns to infer mid-range depth priors through an auxiliary supervised hallucination network trained with radar inputs only. A unified PointNet-based architecture fuses stereo radar features with the hallucinated ToF embedding to generate dense Cartesian point clouds in real time. A large-scale, high-fidelity multimodal dataset of approximately 50,000 frames is generated in NVIDIA Isaac Sim to emulate realistic UAV sensor configurations. Experimental evaluations demonstrate strong robustness to radar noise and reliable geometric consistency suitable for downstream tasks such as ICP-based odometry. The resulting framework delivers LiDAR-like geometric structure using only radar at runtime, offering a computationally efficient, deployable, and resilient solution for UAV navigation in GPS-denied and harsh environmental conditions.