Galeote-Luque Andres, Ruiz-Sarmiento Jose-Raul, Gonzalez-Jimenez Javier
Machine Perception and Intelligent Robotics Group (MAPIR-UMA), Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 Malaga, Spain.
Sensors (Basel). 2022 Sep 15;22(18):6976. doi: 10.3390/s22186976.
In this paper we present a new way to compute the odometry of a 3D lidar in real-time. Due to the significant relation between these sensors and the rapidly increasing sector of autonomous vehicles, 3D lidars have improved in recent years, with modern models producing data in the form of range images. We take advantage of this ordered format to efficiently estimate the trajectory of the sensor as it moves in 3D space. The proposed method creates and leverages a flatness image in order to exploit the information found in flat surfaces of the scene. This allows for an efficient selection of planar patches from a first range image. Then, from a second image, keypoints related to said patches are extracted. This way, our proposal computes the ego-motion by imposing a coplanarity constraint between pairs <point, plane> whose correspondences are iteratively updated. The proposed algorithm is tested and compared with state-of-the-art ICP algorithms. Experiments show that our proposal, running on a single thread, can run 5× faster than a multi-threaded implementation of GICP, while providing a more accurate localization. A second version of the algorithm is also presented, which reduces the drift even further while needing less than half of the computation time of GICP. Both configurations of the algorithm run at frame rates common for most 3D lidars, 10 and 20 Hz on a standard CPU.
在本文中，我们提出了一种实时计算三维激光雷达里程计的新方法。由于这些传感器与快速发展的自动驾驶汽车领域有着密切的关系，近年来三维激光雷达有了改进，现代型号以距离图像的形式产生数据。我们利用这种有序格式来有效地估计传感器在三维空间中移动时的轨迹。所提出的方法创建并利用一个平面度图像，以便利用场景平面表面中发现的信息。这允许从第一距离图像中有效地选择平面块。然后，从第二幅图像中提取与所述块相关的关键点。通过这种方式，我们的方案通过在对应关系被迭代更新的<点，平面>对之间施加共面约束来计算自我运动。所提出的算法经过测试，并与最先进的ICP算法进行了比较。实验表明，我们的方案在单线程上运行时，速度比GICP的多线程实现快5倍，同时提供更精确的定位。还提出了该算法的第二个版本，它进一步减少了漂移，同时所需的计算时间不到GICP的一半。该算法的两种配置都以大多数三维激光雷达常见的帧率运行，在标准CPU上为10赫兹和20赫兹。
