IMU-Aided Registration of MLS Point Clouds Using Inertial Trajectory Error Model and Least Squares Optimization

Zhipeng Chen,Jianwei Yu,Yu Yin,Shiwang Lv,Qingquan Li,Anbang Liang,Dejin Zhang,Jiayuan Li

doi:10.3390/rs14061365

Abstract

Mobile laser scanning (MLS) point cloud registration plays a critical role in mobile 3D mapping and inspection, but conventional point cloud registration methods for terrain LiDAR scanning (TLS) are not suitable for MLS. To cope with this challenge, we use inertial measurement unit (IMU) to assist registration and propose an MLS point cloud registration method based on an inertial trajectory error model. First, we propose an error model of inertial trajectory over a short time period to construct the constraints between trajectory points at different times. On this basis, a relationship between the point cloud registration error and the inertial trajectory error is established, then trajectory error parameters are estimated by minimizing the point cloud registration error using the least squares optimization. Finally, a reliable and concise inertial-assisted MLS registration algorithm is realized. We carried out experiments in three different scenarios: indoor, outdoor and integrated indoor–outdoor. We evaluated the overall performance, accuracy and efficiency of the proposed method. Compared with the ICP method, the accuracy and speed of the proposed method were improved by 2 and 2.8 times, respectively, which verified the effectiveness and reliability of the proposed method. Furthermore, experimental results show the significance of our method in constructing a reliable and scalable mobile 3D mapping system suitable for complex scenes.

Highlights

(1) We propose a new inertial trajectory error model, which constructs constraints between LiDAR scan frames in a shorttime window, and at the same time avoids the effect of long-term inertial measurement unit (IMU) drift on registration accuracy
Due to IMU device error and measurement noise, the trajectory error of IMU integration alone will diverge with time, so the trajectory of IMU integration cannot be directly used for the registration of sequence point clouds over a long time period (Figure 2)
We propose an inertial trajectory error model over a short time period to construct constraints for point cloud registration, minimize the point cloud error through least squares optimization, and achieve reliable Mobile laser scanning (MLS) point cloud registration

Summary

Introduction

Large-scene 3D point cloud data acquisition is one of the foundations of 3D mapping and inspection [1], and mobile LiDAR scanning (MLS) technology provides an accurate and efficient way to obtain large-scene 3D point cloud data [2]. MLS technology uses LiDAR installed on a mobile platform to scan the environment. Due to the limited scanning range of a single LiDAR frame, it is necessary to convert the scan frames at different positions into a unified coordinate system to merge a large-scene 3D point cloud. This registration process plays a critical role in MLS data processing [3]. Position and attitude in the unified mapping frame are given to each laser point according to the sampling time to realize

Methods

Results

Discussion

Conclusion