QiXin Wang, Mingju Wang
Information Department, Shiyan Taihe Hospital (Affiliated Hospital of Hubei Medical College), Shiyan, HuBei Province, China.
PeerJ Comput Sci. 2024 Jul 17;10:e2189. doi: 10.7717/peerj-cs.2189. eCollection 2024.
Self-localization and pose registration are required for sound operation of next generation autonomous vehicles under uncertain environments. Thus, precise localization and mapping are crucial tasks in odometry, planning and other downstream processing. In order to reduce information loss in preprocessing, we propose leveraging LiDAR-based localization and mapping (LOAM) with point cloud-based deep learning instead of convolutional neural network (CNN) based methods that require cylindrical projection. The normal distribution transform (NDT) algorithm is then used to refine the former coarse pose estimation from the deep learning model. The results demonstrate that the proposed method is comparable in performance to recent benchmark studies. We also explore the possibility of using Product Quantization to improve NDT internal neighborhood searching by using high-level features as fingerprints.
在不确定环境下，自定位和位姿配准是下一代自动驾驶车辆正常运行所必需的。因此，精确的定位和建图是里程计、规划及其他下游处理中的关键任务。为了减少预处理中的信息损失，我们提出利用基于激光雷达的定位与建图（LOAM）以及基于点云的深度学习，而非需要柱面投影的基于卷积神经网络（CNN）的方法。然后使用正态分布变换（NDT）算法对深度学习模型的初步粗略位姿估计进行优化。结果表明，该方法的性能与近期的基准研究相当。我们还探讨了使用乘积量化以利用高级特征作为指纹来改进NDT内部邻域搜索的可能性。
