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).

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，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。为此，我们在Kimera基础上进行扩展，使其能够使用多相机以及外部（例如车轮）里程计传感器，从而在实际问题中获得准确且鲁棒的里程计估计。此外，我们提出了一种有效的闭环方案，避免了基于透视n点方法的常见替代方案的缺点，并且也适用于单目相机。最后，我们开发了一种自由空间稠密3D地图构建方法，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原型车采集的多个室内外停车场景真实数据集以及照片级真实感仿真中测试了系统。实验结果表明，我们的多相机系统优于最先进的开源视觉惯性SLAM流程（Vins-Fusion、ORB-SLAM3），在超过8公里行驶距离（所有数据集合计）中，平均轨迹误差低于轨迹长度的1%。