Radar and OpenStreetMap-Aided Consistent Trajectory Estimation in Canopy-Occluded Environments

Accurate localization in canopy-occluded, GNSS-challenged environments is critical for autonomous robots and intelligent vehicles. This paper presents a coarse-to-fine trajectory estimation framework using millimeter-wave radar as the primary sensor, leveraging its foliage penetration and robustness to low visibility. The framework integrates short- and long-term temporal feature enhancement to improve descriptor distinctiveness and suppress false loop closures, together with adaptive OpenStreetMap-derived priors that provide complementary global corrections in scenarios with sparse revisits. All constraints are jointly optimized within an outlier-robust backend to ensure global trajectory consistency under severe GNSS signal degradation. Evaluations conducted on the MulRan dataset, the OORD forest canopy dataset, and real-world campus experiments with partial and dense canopy coverage demonstrate up to 55.23% reduction in Absolute Trajectory Error (ATE) and a minimum error of 1.83 m compared with baseline radar- and LiDAR-based SLAM systems. The results indicate that the integration of temporally enhanced radar features with adaptive map constraints substantially improves large-scale localization robustness.