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

Yu Wang¹, Qiang Zou¹, Fei Wang¹

¹Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China.

Frontiers in Neurorobotics

|July 16, 2026

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This study enhances distributed multi-robot SLAM for outdoor navigation by reducing point cloud data and adapting ground segmentation for sloped terrain, significantly improving robot localization accuracy and efficiency.

Area of Science:

Robotics
Simultaneous Localization and Mapping (SLAM)

Background:

Distributed multi-robot SLAM systems struggle in complex outdoor environments.
Challenges include redundant ground points and fixed segmentation thresholds on sloped terrain.

Purpose of the Study:

To improve large-scale outdoor robot navigation using distributed SLAM.
To address challenges of data redundancy and terrain variability in SLAM.

Main Methods:

Integrated parallel ground segmentation preprocessing to reduce point cloud size.
Developed Slope-Adaptive Ground Segmentation (SAGS) using IMU data for dynamic parameter adjustment.

Main Results:

Reduced point cloud size by 50.78%, improving RMSE by 21.4% for Robot 0.

Keywords:

IMU-guided parameter tuning LiDAR odometry distributed multi-robot SLAM field robotics ground segmentation slope adaptation

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SAGS improved Robot 1 RMSE by 26.5% on sloped terrain, achieving 1.083 m RMSE on a benchmark.

Conclusions:

Ground segmentation preprocessing and SAGS modules independently enhance distributed SLAM performance.
The proposed methods offer robust and efficient outdoor robot navigation solutions.