The emergence of modern RGB-D sensors had a significant impact in many application fields, including robotics, augmented reality (AR) and 3D scanning. They are low-cost, low-power and low-size alternatives to traditional range sensors such as LiDAR. Moreover, unlike RGB cameras, RGB-D sensors provide the additional depth information that removes the need of frame-by-frame triangulation for 3D scene reconstruction. These merits have made them very popular in mobile robotics and AR, where it is of great interest to estimate ego-motion and 3D scene structure. Such spatial understanding can enable robots to navigate autonomously without collisions and allow users to insert virtual entities consistent with the image stream. In this chapter, we review common formulations of odometry and Simultaneous Localization and Mapping (known by its acronym SLAM) using RGB-D stream input. The two topics are closely related, as the former aims to track the incremental camera motion with respect to a local map of the scene, and the latter to jointly estimate the camera trajectory and the global map with consistency. In both cases, the standard approaches minimize a cost function using nonlinear optimization techniques. This chapter consists of three main parts: In the first part, we introduce the basic concept of odometry and SLAM and motivate the use of RGB-D sensors. We also give mathematical preliminaries relevant to most odometry and SLAM algorithms. In the second part, we detail the three main components of SLAM systems: camera pose tracking, scene mapping and loop closing. For each component, we describe different approaches proposed in the literature. In the final part, we provide a brief discussion on advanced research topics with the references to the state-of-the-art.

中文翻译：

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RGB-D 里程计和 SLAM

现代 RGB-D 传感器的出现对许多应用领域产生了重大影响，包括机器人、增强现实 (AR) 和 3D 扫描。它们是传统距离传感器（如 LiDAR）的低成本、低功耗和小尺寸替代品。此外，与 RGB 相机不同，RGB-D 传感器提供额外的深度信息，从而无需逐帧进行 3D 场景重建。这些优点使它们在移动机器人和 AR 中非常受欢迎，在这些领域中，估计自我运动和 3D 场景结构非常有趣。这种空间理解可以使机器人在没有碰撞的情况下自主导航，并允许用户插入与图像流一致的虚拟实体。在这一章当中，我们使用 RGB-D 流输入回顾了里程计和同步定位和映射（以其首字母缩写词 SLAM 已知）的常见公式。这两个主题密切相关，因为前者旨在跟踪相对于场景局部地图的增量相机运动，而后者则以一致的方式联合估计相机轨迹和全局地图。在这两种情况下，标准方法都使用非线性优化技术来最小化成本函数。本章由三个主要部分组成：在第一部分中，我们介绍了里程计和 SLAM 的基本概念，并激发了 RGB-D 传感器的使用。我们还给出了与大多数里程计和 SLAM 算法相关的数学预备知识。在第二部分，我们详细介绍了 SLAM 系统的三个主要组件：相机姿态跟踪、场景映射和循环闭合。对于每个组件，我们描述了文献中提出的不同方法。在最后一部分，我们简要讨论了先进的研究主题，并引用了最新技术。