Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans
This work addresses long-term localization for autonomous vehicles in urban settings, though it is incremental as it builds on existing pole landmark concepts.
The authors tackled vehicle localization in urban environments by developing a system using pole landmarks from 3-D lidar, demonstrating improved long-term reliability over 15 months and higher accuracy compared to a recent method.
Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation at www.github.com/acschaefer/polex. In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the system with localizing a mobile robot over the course of 15~months in an urban area based on an initial map, confronting it with constantly varying routes, differing weather conditions, seasonal changes, and construction sites. Second, we show that the proposed approach clearly outperforms a recently published method in terms of accuracy.