MRGF: A robust SLAM Framework based on Millimeter wave Radar and GNSS Fusion in Harsh Environments

Abstract. Maritime vehicles face significant positioning challenges under adverse weather conditions where visual and laser SLAM systems suffer from severe degradation. Millimeter-wave radar offers inherent robustness to weather interference, yet single-band radar cannot simultaneously achieve accurate translation and robust attitude estimation.This paper proposes a complementary fusion framework for multi-band radar odometry.This system leverages W-band radar (CFEAR) for reliable translation estimation and combines it with X-band radar (LodeStar) to improve rotational estimation robustness. The main innovations are as follows:(1) A complementary fusion framework exploiting the complementary characteristics of W-band and X-band radar; (2) A quality-aware adaptive weighting mechanism dynamically computing fusion weights based on sensor data quality assessment; (3) A consistency gating mechanism monitoring inter-sensor agreement and activating protective measures during sensor degradation.Experiments on the MOANA maritime dataset demonstrate that the proposed method achieves stable and reliable local motion estimation, reaching an RTE RMSE of 1.67 m on the Near-Port sequence.