Abstract. Urban scene understanding requires both global geographic context and local structural detail. Earth Observation (EO) imagery supports large-scale land-cover and land-use (LCLU) mapping, but in urban areas it often merges heterogeneous surfaces into broad built-up classes. Vehicle-based sensors such as LiDAR and cameras recover these local structures, but their maps can drift and often remain in a local coordinate frame. We present urban graph, which combines overhead EO priors, vehicle observations, and fixed roadside anchors in a hierarchical semantic scene graph. Coarse georeferenced regions from EO data are updated with local observations, while a factor graph jointly optimises SLAM constraints and global geodetic constraints. The resulting graph is projected back to the overhead layer to separate coarse urban classes into finer semantic components. Experiments in CARLA show improved global alignment, reduced drift, and more detailed projection of local semantics into EO space.
Minghao Yu, Chenyang Wang, Youchen Tang et al.· The International Archives o...· 0 citations
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.
Fangcheng Qu, Yuheng Zhang, Xianlang Wei et al.· The International Archives o...· 0 citations
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