面向自主代客泊车的多相机视觉-惯性同时定位与建图
Multi-Camera Visual-Inertial Simultaneous Localization and Mapping for Autonomous Valet Parking
AI总结：
本文扩展 Kimera 构建多相机视觉-惯性 SLAM 系统，提出单目可用的闭环方案和结合分割网络与单应性技术的自由空间稠密建图方法，在福特汽车真实数据及仿真中实现低于 1% 的平均轨迹误差，优于 Vins-Fusion 和 ORB-SLAM3。
摘要：
定位与建图是自动驾驶车辆的关键能力。在本文中，我们在 Kimera 的基础上进行构建，并将其扩展为使用多个相机以及外部（例如车轮）里程计传感器，以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，该方案规避了基于 Perspective-n-Point 方法的常见替代方案的缺点，并且也适用于单个单目相机。最后，我们开发了一种用于自由空间的稠密 3D 建图方法，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建图技术相结合。我们在照片级真实感仿真以及由福特汽车公司开发的汽车原型上采集的多个真实数据集上测试了我们的系统，涵盖室内和室外停车场景。我们的多相机系统被证明优于最先进的开源视觉-惯性 SLAM 流程（Vins-Fusion、ORB-SLAM3），并且在超过 8 公里的行驶距离（所有数据集合计）中，平均轨迹误差低于轨迹长度的 1%。展示该系统的视频可在以下地址获取：youtu.be/H8CpzDpXOI8。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
