论文标题
自动驾驶汽车的长期地图维护管道
Long-term map maintenance pipeline for autonomous vehicles
论文作者
论文摘要
为了使自动驾驶汽车在典型的城市环境中持续运行，必须拥有高准确的位置信息。这需要一个可以随时间变化的映射和本地化系统。基于单个调查图的本地化方法将不适合长期操作，因为它不包含环境中的变化。在本文中，我们提出了新算法，以维护基于特色的地图。提出了一条地图维护管道，该管道可以通过最相关的功能不断地更新地图，以利用周围环境的变化。我们的管道根据其与车辆姿势的几何关系来检测并去除瞬态特征。新确定的功能成为新功能图的一部分，并由管道评估为本地化图的候选人。通过清除过时的功能并添加新检测到的功能，我们将不断更新以前的地图以更准确地代表最新环境。我们已经使用USYD校园数据集验证了我们的方法，该数据集包括超过18个月的数据。提出的结果表明，我们的维护管道会产生一个弹性的图，可以随着时间的推移提供持续的本地化性能。
For autonomous vehicles to operate persistently in a typical urban environment, it is essential to have high accuracy position information. This requires a mapping and localisation system that can adapt to changes over time. A localisation approach based on a single-survey map will not be suitable for long-term operation as it does not incorporate variations in the environment. In this paper, we present new algorithms to maintain a featured-based map. A map maintenance pipeline is proposed that can continuously update a map with the most relevant features taking advantage of the changes in the surroundings. Our pipeline detects and removes transient features based on their geometrical relationships with the vehicle's pose. Newly identified features became part of a new feature map and are assessed by the pipeline as candidates for the localisation map. By purging out-of-date features and adding newly detected features, we continually update the prior map to more accurately represent the most recent environment. We have validated our approach using the USyd Campus Dataset, which includes more than 18 months of data. The results presented demonstrate that our maintenance pipeline produces a resilient map which can provide sustained localisation performance over time.
