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

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.