论文标题
自动驾驶汽车的视觉定位持续地图节省：Orb-Slam扩展
Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension
论文作者
论文摘要
电动Vhicles和自动驾驶主导了汽车领域的当前研究工作。这两个主题在使更安全，更环保的驾驶方面齐头并进。自动驾驶汽车的一个基本构建块是能够建造环境地图并在此地图上进行本地化的能力。在本文中，我们利用立体声相机传感器来感知环境并创建地图。随着实时的同时定位和映射（SLAM），由于没有地面真实图被用作参考，并且随着时间的推移会累积错误，因此存在错误定位的风险。因此，我们首先在低驾驶速度下构建并保存环境的视觉特征图，并将其扩展到Orb-Slam \，2个包装。在第二次运行中，我们重新加载地图，然后本地化在先前构建的地图上。与完整的大满贯相比，在先前构建的地图上的加载和本地化可以提高自动驾驶汽车的连续定位精度。原始的Orb-Slam \ 2实现中缺少此地图保存功能。 我们评估了Kitti数据集场景的本地化准确性，可针对构建的大满贯地图。此外，我们测试了使用自己的小型电动模型汽车记录的数据的本地化。我们表明，在功能丰富的环境中，以平均纵向速度为36 m/s的车辆，本地化的相对翻译误差保持在1 \％以下。与完整的大满贯相比，本地化模式有助于更好的本地化精度和更低的计算负载。我们对Orb-Slam2贡献的源代码将在以下网址公开：https：//github.com/tumftm/orbslam-map-saving-extension。
Electric vhicles and autonomous driving dominate current research efforts in the automotive sector. The two topics go hand in hand in terms of enabling safer and more environmentally friendly driving. One fundamental building block of an autonomous vehicle is the ability to build a map of the environment and localize itself on such a map. In this paper, we make use of a stereo camera sensor in order to perceive the environment and create the map. With live Simultaneous Localization and Mapping (SLAM), there is a risk of mislocalization, since no ground truth map is used as a reference and errors accumulate over time. Therefore, we first build up and save a map of visual features of the environment at low driving speeds with our extension to the ORB-SLAM\,2 package. In a second run, we reload the map and then localize on the previously built-up map. Loading and localizing on a previously built map can improve the continuous localization accuracy for autonomous vehicles in comparison to a full SLAM. This map saving feature is missing in the original ORB-SLAM\,2 implementation. We evaluate the localization accuracy for scenes of the KITTI dataset against the built up SLAM map. Furthermore, we test the localization on data recorded with our own small scale electric model car. We show that the relative translation error of the localization stays under 1\% for a vehicle travelling at an average longitudinal speed of 36 m/s in a feature-rich environment. The localization mode contributes to a better localization accuracy and lower computational load compared to a full SLAM. The source code of our contribution to the ORB-SLAM2 will be made public at: https://github.com/TUMFTM/orbslam-map-saving-extension.
