【摘要】 城市环境中的精确定位是自动驾驶领域的重点和难点,现有的激光雷达定位算法虽然能够在多数情况下保持较高的精度,但在一些比较复杂的城市动态场景中仍存在问题。针对这类场景中遮挡导致的全球定位系统定位精度下降,以及运动目标和环境变化导致的有效点云特征减少的问题,提出一个新的概率定位框架;该框架使用核密度估计的方法对改进后的多层次随机采样一致性算法和直方图滤波算法进行融合,以有效克服多层次随机采样一致性算法在部分场景中的定位波动问题,以及直方图滤波算法在位姿误差较大时的效率低下和局部最优问题。结果表明:所提框架在保证定位精度的前提下,提升了对动态密集场景的适用性,能够在现有算法容易出错的场景中实现更加稳定精确的定位,并能够容忍更大的初始位姿误差。更多还原

【Abstract】 Achieving high-accuracy localization in urban environments is challenging in autonomous driving.The existing LiDAR-based localization algorithms can ensure high accuracy in most cases;however,the localization problems in complex dynamic city scenes still need to be addressed.This study proposes a novel probabilistic localization framework to mitigate the accuracy degradation of the global positioning system caused by occlusion and to reduce the effective point cloud features caused by moving objects and changing environments in such scenarios.The proposed framework combines the improved multi-layer random sample consensus algorithm and the histogram filtering algorithm with the kernel density estimation method;this combination effectively overcomes the localization fluctuation of multi-layer random sample consensus in some scenes as well as the inefficiency and local optimum of histogram filtering when the pose error is large.The experimental results indicate that the proposed framework can provide more stable and accurate localization as well as tolerate larger initial pose errors compared with the existing localization methods when applied to complex dynamic city scenes.更多还原