With the rapid development of autonomous driving and SLAM technology, the
performance of autonomous systems using multimodal sensors highly relies on
accurate extrinsic calibration. Addressing the need for a convenient,
maintenance-friendly calibration process in any natural environment, this paper
introduces Galibr, a fully automatic targetless LiDAR-camera extrinsic
calibration tool designed for ground vehicle platforms in any natural setting.
The method utilizes the ground planes and edge information from both LiDAR and
camera inputs, streamlining the calibration process. It encompasses two main
steps: an initial pose estimation algorithm based on ground planes (GP-init),
and a refinement phase through edge extraction and matching. Our approach
significantly enhances calibration performance, primarily attributed to our
novel initial pose estimation method, as demonstrated in unstructured natural
environments, including on the KITTI dataset and the KAIST quadruped dataset.

该论文介绍了一种便捷、易于维护的全自动无目标 LiDAR - 相机外参标定工具 Galibr，用于地面车辆平台在任何自然环境下进行标定，通过利用 LiDAR 和相机输入的地面平面和边缘信息，流线化标定过程，该方法包括两个主要步骤：基于地面平面的初始位姿估计算法 (GP-init) 和通过边缘提取和匹配的细化阶段，在 KITTI 数据集和 KAIST 四足数据集等非结构化自然环境中展示了我们的方法明显提升了标定性能，主要归功于我们的初始位姿估计方法。

Galibr：基于地面平面初始化的无目标 LiDAR - 相机外参标定方法

Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground  Plane Initialization

In addition to environmental perception sensors such as cameras, radars, etc.
in the automatic driving system, the external environment of the vehicle is
perceived, in fact, there is also a perception sensor that has been silently
dedicated in the system, that is, the positioning module. This paper explores
the application of SLAM (Simultaneous Localization and Mapping) technology in
the context of automatic lane change behavior prediction and environment
perception for autonomous vehicles. It discusses the limitations of traditional
positioning methods, introduces SLAM technology, and compares LIDAR SLAM with
visual SLAM. Real-world examples from companies like Tesla, Waymo, and Mobileye
showcase the integration of AI-driven technologies, sensor fusion, and SLAM in
autonomous driving systems. The paper then delves into the specifics of SLAM
algorithms, sensor technologies, and the importance of automatic lane changes
in driving safety and efficiency. It highlights Tesla's recent update to its
Autopilot system, which incorporates automatic lane change functionality using
SLAM technology. The paper concludes by emphasizing the crucial role of SLAM in
enabling accurate environment perception, positioning, and decision-making for
autonomous vehicles, ultimately enhancing safety and driving experience.

研究论文探讨了在自动驾驶系统中应用 SLAM 技术（同时定位与地图构建），以实现自动车道变换行为预测和环境感知。通过比较激光雷达 SLAM 和视觉 SLAM，介绍了传统定位方法的局限性，并展示了特斯拉、Waymo 和 Mobileye 等公司在自动驾驶系统中应用人工智能技术、传感器融合和 SLAM。研究还深入探讨了 SLAM 算法、传感器技术以及自动车道变换在驾驶安全和效率中的重要性。论文强调了 SLAM 在实现准确的环境感知、定位和决策方面的关键作用，从而提升安全性和驾驶体验。