Simultaneous Localization And Mapping (SLAM) is a task to estimate the robot location and to reconstruct the environment based on observation from sensors such as LIght Detection And Ranging (LiDAR) and camera. It is widely used in robotic applications such as autonomous driving and drone delivery. Traditional LiDAR-based SLAM algorithms mainly leverage the geometric features from the scene context, while the intensity information from LiDAR is ignored. Some recent deep-learning-based SLAM algorithms consider intensity features and train the pose estimation network in an end-to-end manner. However, they require significant data collection effort and their generalizability to environments other than the trained one remains unclear. In this letter we introduce intensity features to a SLAM system. And we propose a novel full SLAM framework that leverages both geometry and intensity features. The proposed SLAM involves both intensity-based front-end odometry estimation and intensity-based back-end optimization. Thorough experiments are performed including both outdoor autonomous driving and indoor warehouse robot manipulation. The results show that the proposed method outperforms existing geometric-only LiDAR SLAM methods.

中文翻译：

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Intensity-SLAM：用于大规模环境的强度辅助定位和制图

同步定位和地图绘制（SLAM）是一项任务，用于根据来自诸如激光测距和测距（LiDAR）和相机等传感器的观察来估计机器人的位置并重建环境。它被广泛用于机器人应用，例如自动驾驶和无人机交付。传统的基于LiDAR的SLAM算法主要利用场景上下文中的几何特征，而忽略来自LiDAR的强度信息。一些最近的基于深度学习的SLAM算法考虑强度特征并以端到端的方式训练姿势估计网络。但是，它们需要大量的数据收集工作，并且尚不清楚它们是否适用于除受过训练的人员之外的其他环境。在这封信中，我们向SLAM系统介绍了强度特征。我们提出了一个新颖的完整SLAM框架，该框架充分利用了几何和强度特征。提出的SLAM涉及基于强度的前端里程计估计和基于强度的后端优化。进行了包括室外自动驾驶和室内仓库机器人操纵在内的全面实验。结果表明，该方法优于现有的仅几何LiDAR SLAM方法。