Localization and mapping are key capabilities for self-driving vehicles. This paper describes a visual-inertial SLAM system that estimates an accurate and globally consistent trajectory of the vehicle and reconstructs a dense model of the free space surrounding the car. Towards this goal, we build on Kimera and extend it to use multiple cameras as well as external (e.g. 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 by 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 8 km of distance traveled (combined across all datasets). A video showcasing the system is available here: youtu.be/H8CpzDpXOI8

翻译：定位与地图构建是自动驾驶车辆的关键能力。本文描述了一种视觉-惯性SLAM系统，该系统能够估计车辆精确且全局一致的轨迹，并重建车辆周围自由空间的稠密模型。为实现此目标，我们基于Kimera框架进行扩展，使其支持多相机以及外部（如车轮）里程计传感器，从而在实际问题中获得精确且鲁棒的里程计估计。此外，我们提出了一种有效的闭环检测方案，该方案避免了基于透视n点方法的常见替代方案的缺陷，且同样适用于单目相机。最后，我们开发了一种自由空间稠密三维地图构建方法，该方法将用于自由空间检测的分割网络与基于单应性的稠密地图构建技术相结合。我们在福特汽车公司开发的汽车原型所采集的多个真实数据集（涵盖室内外停车场景）以及逼真仿真环境中对系统进行了测试。实验表明，我们的多相机系统优于当前最先进的开源视觉-惯性SLAM系统（Vins-Fusion、ORB-SLAM3），并在超过8公里（所有数据集合计）的行驶距离上实现了平均轨迹误差低于轨迹长度的1%。展示该系统的视频可在此处获取：youtu.be/H8CpzDpXOI8