Distributed multi-robot LiDAR SLAM with ground-optimized preprocessing and slope-adaptive segmentation

Large-scale outdoor robot navigation increasingly demands SLAM systems capable of operating efficiently across diverse and challenging terrain. While single-robot approaches face inherent coverage and computational limitations, distributed multi-robot frameworks extend this capability through collaborative mapping-yet they still degrade in complex outdoor environments due to two unresolved challenges: redundant ground points in raw point clouds overload feature extraction and loop-closure matching, while fixed ground segmentation thresholds fail on sloped terrain causing misclassification and trajectory degradation. We address the first challenge by integrating ground segmentation preprocessing as a parallel stage for each robot within the distributed SLAM framework, reducing point cloud size by 50.78% and achieving a 21.4% RMSE improvement for Robot 0 (7.99 m → 6.28 m) compared to the unprocessed baseline. We address the second challenge with the proposed SAGS (Slope-Adaptive Ground Segmentation) module, which continuously monitors platform tilt via IMU orientation and dynamically interpolates ground segmentation parameters within a 5°-15° tilt range; SAGS recovers Robot 1 RMSE from 8.48 m to 6.23 m (26.5% improvement) on sloped terrain without flat-terrain penalty (GPS-validated 1.083 m RMSE on a public 612 m benchmark). Both contributions are validated through progressive three-stage ablation evaluation on a campus three-robot dataset (heterogeneous team: two wheeled ground robots and one legged quadruped, diverse terrain including sloped sections) and cross-validated on a public GPS benchmark (612 m, GPS ground truth), confirming the independent contribution of each system component.