Graph SLAM correction for single scanner MLS forest data under boreal forest canopy

Abstract

Mobile laser scanning (MLS) provides kinematic means to collect three dimensional data from surroundings for various mapping and environmental analysis purposes. Vehicle based MLS has been used for road and urban asset surveys for about a decade. The equipment to derive the trajectory information for the point cloud generation from the laser data is almost without exception based on GNSS-IMU (Global Navigation Satellite System – Inertial Measurement Unit) technique. That is because of the GNSS ability to maintain global accuracy, and IMU to produce the attitude information needed to orientate the laser scanning and imaging sensor data. However, there are known challenges in maintaining accurate positioning when GNSS signal is weak or even absent over long periods of time. The duration of the signal loss affects the severity of degradation of the positioning solution depending on the quality/performance level of the IMU in use.The situation could be improved to a certain extent with higher performance IMUs, but increasing system expenses make such approach unsustainable in general. Another way to tackle the problem is to attach additional sensors to the system to overcome the degrading position accuracy: such that observe features from the environment to solve for short term system movements accurately enough to prevent the IMU solution to drift. This results in more complex system integration with need for more calibration and synchronization of multiple sensors into an operational approach.In this paper we study operation of an ATV (All -terrain vehicle) mounted, GNSS-IMU based single scanner MLS system in boreal forest conditions. The data generated by RoamerR2 system is targeted for generating 3D terrain and tree maps for optimizing harvester operations and forest inventory purposes at individual tree level. We investigate a process-flow and propose a graph optimization based method which uses data from a single scanner MLS for correcting the post-processed GNSS-IMU trajectory for positional drift under mature boreal forest canopy conditions. The result shows that we can improve the internal conformity of the data significantly from 0.7m to 1cm based on tree stem feature location data. When the optimization result is compared to reference at plot level we reach down to 6cm mean error in absolute tree stem locations. The approach can be generalized to any MLS point cloud data, and provides as such a remarkable contribution to harness MLS for practical forestry and high precision terrain and structural modeling in GNSS obstructed environments.