Trusted Position for 5G-Connected Urban Robots Based on LiDAR-IMU Detection and QPV-Based Verification Bounds

Abstract

Urban service robots in dense urban centers require reliable onboard perception and a trustworthy position claim within connected mobile infrastructure. This work presents a Trusted Position framework integrating a validated lightweight dynamic obstacle detection (DOD) pipeline with 5G connectedness characterization using ns-3/5G-LENA multi-cell simulations under realistic SUMO-based mobility. Quantum Position Verification (QPV) is incorporated as an abstract timing-constrained positional authentication layer. The DOD pipeline sustains 10-15 Hz operation with high static filtering accuracy. Network simulations reveal a throughput-timeliness trade-off: TDMA (Time-Division Multiple Access) schedulers maintain sub-10 ms delivery, whereas OFDMA (Orthogonal Frequency-Division Multiple Access) schedulers increase delays to 85-1505 ms, exceeding freshness budgets for local perception coherence. The results define three trust levels: TDMA trusted, OFDMA MR (Maximum Rate) conditionally trusted, and OFDMA PF (Proportional Fair)/RR (Round Robin) untrusted. Under the studied conditions, the proposed trusted positioning is achievable with TDMA within the proposed timing-bound model.