Leveraging Prior ALS Point Clouds for Enhanced LiDAR-Inertial Odometry in Low-Cost Mobile Mapping Systems

Yandi Yang, Jianping Li, Mengchi Ai, Xin Zhao, Yizhe Zhang, and Naser El-Sheimy

Peer Reviewed

Abstract:	LIO(LiDAR-Inertial-Odometry) performs poorly in highly-urbanized scenarios. This paper proposes an enhanced LIO method by utilizing existing ALS point clouds to improve the accuracy and robustness of localization for low-cost MMS (Mobile Mapping System). ALS and MLS point clouds are preprocessed separately. Then the LIO with multiple scan-to-map matchings is maintained. The framework’s performance was validated through experiments on both large-scale vehicle-mounted and hand-held datasets. The average APE (Absolute Pose Error) is only 1 meter even with a trajectory over 4 km and IMU collection time over 50 minutes. To the best of our knowledge, this marks the first successful attempt at achieving real-time and accurate LIO for low-cost MMS combined with ALS data in large-scale outdoor environments. Keywords—LIO, SLAM, air-ground collaboration
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	643 - 647
Cite this article:	Yang, Yandi, Li, Jianping, Ai, Mengchi, Zhao, Xin, Zhang, Yizhe, El-Sheimy, Naser, "Leveraging Prior ALS Point Clouds for Enhanced LiDAR-Inertial Odometry in Low-Cost Mobile Mapping Systems," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 643-647.
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