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2D lidar to kinematic chain calibration using planar features of indoor scenes
Industrial Robot ( IF 1.8 ) Pub Date : 2020-06-04 , DOI: 10.1108/ir-09-2019-0201
Bernardo Lourenço , Tiago Madeira , Paulo Dias , Vitor M. Ferreira Santos , Miguel Oliveira

Purpose

2D laser rangefinders (LRFs) are commonly used sensors in the field of robotics, as they provide accurate range measurements with high angular resolution. These sensors can be coupled with mechanical units which, by granting an additional degree of freedom to the movement of the LRF, enable the 3D perception of a scene. To be successful, this reconstruction procedure requires to evaluate with high accuracy the extrinsic transformation between the LRF and the motorized system.

Design/methodology/approach

In this work, a calibration procedure is proposed to evaluate this transformation. The method does not require a predefined marker (commonly used despite its numerous disadvantages), as it uses planar features in the point acquired clouds.

Findings

Qualitative inspections show that the proposed method reduces artifacts significantly, which typically appear in point clouds because of inaccurate calibrations. Furthermore, quantitative results and comparisons with a high-resolution 3D scanner demonstrate that the calibrated point cloud represents the geometries present in the scene with much higher accuracy than with the un-calibrated point cloud.

Practical implications

The last key point of this work is the comparison of two laser scanners: the lemonbot (authors’) and a commercial FARO scanner. Despite being almost ten times cheaper, the laser scanner was able to achieve similar results in terms of geometric accuracy.

Originality/value

This work describes a novel calibration technique that is easy to implement and is able to achieve accurate results. One of its key features is the use of planes to calibrate the extrinsic transformation.



中文翻译:

使用室内场景的平面特征进行2D激光雷达到运动链的校准

目的

2D激光测距仪(LRF)是机器人技术领域中常用的传感器,因为它们可提供高角度分辨率的精确距离测量。这些传感器可以与机械单元耦合,通过为LRF的移动赋予额外的自由度,可以实现场景的3D感知。为了成功,此重建过程需要以高精度评估LRF和电动系统之间的外部转换。

设计/方法/方法

在这项工作中,提出了一个校准程序来评估这种转换。该方法不需要预定义的标记(尽管存在许多缺点,但通常会使用),因为它在点采集的云中使用了平面特征。

发现

定性检查表明,所提出的方法可显着减少伪影,由于校准不正确,这些伪影通常出现在点云中。此外,定量结果和与高分辨率3D扫描仪的比较表明,校准后的点云比未校准的点云具有更高的精度,可以代表场景中存在的几何形状。

实际影响

这项工作的最后一个重点是比较两种激光扫描仪:Lemonbot(作者)和商用FARO扫描仪。尽管价格便宜了近十倍,但激光扫描仪在几何精度方面仍能达到类似的结果。

创意/价值

这项工作描述了一种新颖的校准技术,该技术易于实施并且能够获得准确的结果。它的主要特征之一是使用平面来校准外部变换。

更新日期:2020-06-04
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