论文标题
莫扎德：在城市户外环境中自动驾驶的多模式定位
MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments
论文作者
论文摘要
视觉上差的场景是室外环境中视觉定位系统中故障的主要来源之一。为了应对这一挑战，我们提出了莫扎德（Mozard），这是一种使用Vision和LiDar的多式联运定位系统，适用于城市室外环境。通过使用语义数据扩展我们先前存在的基于关键点的视觉多课程本地化方法，可以在截然不同的外观条件下实现改进的定位召回。特别是，由于它们在城市环境中的广泛分配和可靠性，我们专注于使用Curbstone信息。我们在挑战城市户外环境中进行了几公里的详尽的实验评估，分析我们本地化系统的召回和准确性，并在案例研究中证明每个子系统可能的失败案例。我们证明，莫扎德能够弥合以前工作的情况，从而失败，因此产生了召回性能的提高，而相似的本地化精度则达到0.2m
Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By extending our preexisting key-point based visual multi-session local localization approach with the use of semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular we focus on the use of curbstone information because of their broad distribution and reliability within urban environments. We present thorough experimental evaluations on several driving kilometers in challenging urban outdoor environments, analyze the recall and accuracy of our localization system and demonstrate in a case study possible failure cases of each subsystem. We demonstrate that MOZARD is able to bridge scenarios where our previous work VIZARD fails, hence yielding an increased recall performance, while a similar localization accuracy of 0.2m is achieved
