非结构化道路中的无人驾驶精确定位大量使用基于激光雷达的simultaneous localization and mapping(SLAM)技术，解决因环境变化导致的预建地图匹配失败，进而引起定位丢失的问题一直是业内难题和热点研究方向之一。针对上述问题，提出一种利用激光雷达和惯性测量单元在normal distribution transform(NDT)定位基础上融合实时局部地图匹配的长周期鲁棒定位方法online location normal distributions transform(OL-NDT)。OL-NDT将NDT获得的定位信息作为测量信息因子输入因子图中优化实时构建的局部地图，并且在其全局定位丢失后采用实时局部地图进行定位。在MulRan数据集上进行定位精度测试，OL-NDT的累计误差占比为 0。40%，较现有的传统定位方法降低了 1。06个百分点，定位精度得到了有效提升，且在静态结构发生较大变化的场景下也可以精准定位。同时，利用在北京联合大学采集的校园数据验证了在短暂无地图情况下OL-NDT的定位轨迹精度与已知地图时完全匹配。
Long-Period Localization Method for LiDAR Based on Local Mapping
The precise positioning of driverless vehicles on unstructured roads extensively relies on LiDAR-based simultaneous localization and mapping(SLAM).However,the problem of localization loss,caused by the failure of pre-built map matching due to environmental changes,has been an industry challenge and a popular research direction.To address the aforementioned problems,this study proposes a long-term robust localization method,online location normal distributions transform(OL-NDT),which uses LiDAR and inertial measurement units to combine real-time local map matching based on NDT localization.OL-NDT inputs the localization information obtained by NDT as measurement information factors into the factor map to optimize the local maps constructed in real time and uses real-time local maps for localization after NDT localization is lost.OL-NDT is tested on the MulRan dataset and achieves a cumulative error percentage of 0.40%,which is 1.06 percentage points lower than the existing traditional localization methods.This effectively improves localization accuracy and enables accurate localization in scenarios with significant changes in the static structure.Moreover,the campus data collected by Beijing Union University is used to verify that the localization trajectory accuracy of OL-NDT precisely matches the known map,even in cases of short-term missing maps.
LiDARdriverlesslong-term reliable localizationnormal distribution transform
NETL
NSTL
万方数据
