Direct integration of ALS and MLS for real-time localization and mapping

Abstract

This paper presents a novel real-time fusion pipeline for integrating georeferenced airborne laser scanning (ALS) and online mobile laser scanning (MLS) data to enable accurate localization and mapping in complex natural environments. To address sensor drift caused by relative Light Detection and Ranging (lidar) and inertial measurements, occlusion affecting the Global Navigation Satellite System (GNSS) signal quality, and differences in the fields of view of the sensors, we propose a tightly coupled lidar-inertial registration system with an adaptive, robust Iterated Error-State Extended Kalman Filter (RIEKF). By leveraging ALS-derived prior maps as a global reference, our system effectively refines the MLS registration, even in challenging environments like forests. A novel coarse-to-fine initialization technique is introduced to estimate the initial transformation between the local MLS and global ALS frames using online GNSS measurements. Experimental results in forest environments demonstrate significant improvements in both absolute and relative trajectory accuracy, with relative mean localization errors as low as 0.17 m for a prior map based on dense ALS data and 0.22 m for a prior map based on sparse ALS data. We found that while GNSS does not significantly improve registration accuracy, it is essential for providing the initial transformation between the ALS and MLS frames, enabling their direct and online fusion. The proposed system predicts poses at an inertial measurement unit (IMU) rate of 400 Hz and updates the pose at the lidar frame rate of 10 Hz.