Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron.

Fei Zhu,Zizhu Tian,Yaowei Ma,Yuchun Huang

doi:10.3390/s20071837

Abstract

Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods.

Highlights

Lasers can accurately obtain the depth information of the surrounding environment and form a high-precision point cloud
This paper proposed a flexible, high-precision, and robust calibration method for multiple laser rangefinders (LRFs)
Without the need for other sensors, all that is required for our method is to find a position where all the LRFs can observe the trihedron

Summary

Introduction

Lasers can accurately obtain the depth information of the surrounding environment and form a high-precision point cloud. These devices have been widely applied to various tasks, such as location [1], navigation [2], three-dimension reconstruction [3], etc. LSs can obtain the dense 3D point clouds of the surrounding by shooting and receiving laser pulses in three-dimensional space. A combination of multiple LRFs generates laser points in different directions simultaneously, which can be fused into 3D cloud points. To convert the LRFs’ raw data, which record the distance information in LRF coordinates into a unified world coordinate and form a dense 3D point cloud, the key step is to determine the relationship between different LRFs’

Methods

Discussion

Conclusion