Vehicle odometry is an essential component of an automated driving system as it computes the vehicle's position and orientation. The odometry module has a higher demand and impact in urban areas where the global navigation satellite system (GNSS) signal is weak and noisy. Traditional visual odometry methods suffer from the diverse illumination status and get disparities during pose estimation, which results in significant errors as the error accumulates. Odometry using light detection and ranging (LiDAR) devices has attracted increasing research interest as LiDAR devices are robust to illumination variations. In this survey, we examine the existing LiDAR odometry methods and summarize the pipeline and delineate the several intermediate steps. Additionally, the existing LiDAR odometry methods are categorized by their correspondence type, and their advantages, disadvantages, and correlations are analyzed across-category and within-category in each step. Finally, we compare the accuracy and the running speed among these methodologies evaluated over the KITTI odometry dataset and outline promising future research directions.
翻译:在计算飞行器的位置和方向时,飞行器的测量是自动驾驶系统的一个基本组成部分。在全球导航卫星系统信号薄弱和吵闹的城市地区,odoric 模块的需求量和影响较高。传统的视觉测量方法具有不同的照明状态,在进行估测期间出现差异,结果随着误差的积累而出现重大错误。使用光探测和测距(LiDAR)装置的测量方法引起了越来越多的研究兴趣,因为LIDAR装置对照明变异非常强大。我们在这次调查中,研究现有的LIDAR odoric 方法,总结管道并划定若干中间步骤。此外,现有的LIDAR odoricat方法按其通信类型分类,每个步骤的优点、劣势和相关性分析跨类和内部类别。最后,我们比较了在KITTI odologis 数据集中评估的这些方法的准确性和运行速度,并概述了有希望的未来研究方向。