Localization and mapping are key capabilities for self-driving vehicles. In this paper, we build on Kimera and extend it to use multiple cameras as well as external (eg wheel) odometry sensors, to obtain accurate and robust odometry estimates in real-world problems. Additionally, we propose an effective scheme for closing loops that circumvents the drawbacks of common alternatives based on the Perspective-n-Point method and also works with a single monocular camera. Finally, we develop a method for dense 3D mapping of the free space that combines a segmentation network for free-space detection with a homography-based dense mapping technique. We test our system on photo-realistic simulations and on several real datasets collected on a car prototype developed by the Ford Motor Company, spanning both indoor and outdoor parking scenarios. Our multi-camera system is shown to outperform state-of-the art open-source visual-inertial-SLAM pipelines (Vins-Fusion, ORB-SLAM3), and exhibits an average trajectory error under 1% of the trajectory length across more than 8km of distance traveled (combined across all datasets). A video showcasing the system is available at: youtu.be/H8CpzDpXOI8.

翻译：定位与地图构建是自动驾驶车辆的核心能力。本文基于Kimera框架进行扩展，引入多相机系统及外部（例如车轮）里程计传感器，以在真实场景中实现高精度、高鲁棒性的里程估计。针对闭环检测任务，我们提出一种有效方案，该方案规避了基于透视n点法的常见替代方案的缺陷，并支持单目相机场景。此外，我们开发了一种密集三维自由空间映射方法，该方法将用于自由空间检测的分割网络与基于单应性的密集映射技术相结合。我们在照片级仿真数据及福特汽车公司原型车采集的多个真实数据集（涵盖室内外停车场场景）上测试了系统性能。实验表明，我们的多相机系统在性能上优于当前主流的开源视觉-惯性同步定位与地图构建框架（Vins-Fusion、ORB-SLAM3），在总计超过8公里的行驶距离（所有数据集合并计算）上，平均轨迹误差低于轨迹长度的1%。系统演示视频详见：youtu.be/H8CpzDpXOI8。