Intrinsic Calibration of Multi-Beam LiDARs for Agricultural Robots

Abstract

With the advantages of high measurement accuracy and wide detection range, LiDARs have been widely used in information perception research to develop agricultural robots. However, the internal configuration of the laser transmitter layout changes with increasing sensor working duration, which makes it difficult to obtain accurate measurement with calibration files based on factory settings. To solve this problem, we investigate the intrinsic calibration of multi-beam laser sensors. Specifically, we calibrate the five intrinsic parameters of LiDAR with a nonlinear optimization strategy based on static planar models, which include measured distance, rotation angle, pitch angle, horizontal distance, and vertical distance. Firstly, we establish a mathematical model based on the physical structure of LiDAR. Secondly, we calibrate the internal parameters according to the mathematical model and evaluate the measurement accuracy after calibration. Here, we illustrate the parameter calibration with three steps: planar model estimation, objective function construction, and nonlinear optimization. We also introduce the ranging accuracy evaluation metrics, including the standard deviation of the distance from the laser scanning points to the planar models and the 3σ criterion. Finally, the experimental results show that the ranging error of calibrated sensors can be maintained within 3 cm, which verifies the effectiveness of the laser intrinsic calibration.

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

“…In recent years, with the development of computer technology and edge computing equipment, the sensors used for greenhouse operating environment perception and localization have included ultrasonic technology (Palleja and Landers, 2017;Chen et al, 2018;Lao et al, 2021), ultra-wideband (UWB) technology (Hou et al, 2020;Yao et al, 2021), LiDAR technology (Chen et al, 2019;Zhang et al, 2020;Saha et al, 2022;Sun et al, 2022), and machine vision technology (Nissimov et al, 2015;Wang et al, 2022a;Wang et al, 2022b). Huang et al (2021) designed a robot localization system based on spread spectrum sounds for a greenhouse containing a strawberry ridge and achieved centimeter-level localization accuracy in a small greenhouse.…”