Affiliation:
1. Information Department, Shiyan Taihe Hospital (Affiliated Hospital of Hubei Medical College), Shiyan, HuBei Province, China
Abstract
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.
Reference48 articles.
1. Design of robust path-following control system for self-driving vehicles using extended high-gain observer;Al-Nadawi,2020
2. The inverted multi-index;Babenko,2012
3. Autonomous vehicles: challenges, opportunities, and future implications for transportation policies;Bagloee;Journal of Modern Transportation,2016
4. The normal distributions transform: A new approach to laser scan matching;Biber,2003
5. Multi-view 3D object detection network for autonomous driving;Chen,2017