用于自动驾驶的视觉感知系统
Visual Perception System for Autonomous Driving
AI总结：
本文提出一种基于视觉的自动驾驶感知系统，集成行人等运动目标跟踪预测、相机定位与稀疏建图，并通过仿真和真实数据集验证其性能。
摘要：
近期人们对自动驾驶的兴趣激增，源于其在提升安全性、效率和便利性方面快速发展的能力。自动驾驶技术的一个关键方面是其感知系统，其中的核心算法已经产生了更精确、适用于自动驾驶的算法，包括基于视觉的同步定位与地图构建（SLAM）、目标检测和跟踪算法。本工作介绍了一种用于自动驾驶的基于视觉的感知系统，该系统集成了运动目标的轨迹跟踪与预测以防止碰撞，同时满足自动驾驶的定位和地图构建需求。该系统利用来自行人的运动线索来监测和预测其运动，并同时对环境进行地图构建。这种集成方法解决了相机定位以及场景中其他运动目标的跟踪问题，随后生成稀疏地图以促进车辆导航。通过在仿真数据集和真实世界数据集上进行全面评估，该方法的性能、效率和韧性得到了证实。
英文摘要：
The recent surge in interest in autonomous driving stems from its rapidly developing capacity to enhance safety, efficiency, and convenience. A pivotal aspect of autonomous driving technology is its perceptual systems, where core algorithms have yielded more precise algorithms applicable to autonomous driving, including vision-based Simultaneous Localization and Mapping (SLAMs), object detection, and tracking algorithms. This work introduces a visual-based perception system for autonomous driving that integrates trajectory tracking and prediction of moving objects to prevent collisions, while addressing autonomous driving's localization and mapping requirements. The system leverages motion cues from pedestrians to monitor and forecast their movements and simultaneously maps the environment. This integrated approach resolves camera localization and the tracking of other moving objects in the scene, subsequently generating a sparse map to facilitate vehicle navigation. The performance, efficiency, and resilience of this approach are substantiated through comprehensive evaluations of both simulated and real-world datasets.
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。
