Robustness in Simultaneous Localization and Mapping (SLAM) remains one of the key challenges for the real-world deployment of autonomous systems. SLAM research has seen significant progress in the last two and a half decades, yet many state-of-the-art (SOTA) algorithms still struggle to perform reliably in real-world environments. There is a general consensus in the research community that we need challenging real-world scenarios which bring out different failure modes in sensing modalities. In this paper, we present a novel multi-modal indoor SLAM dataset covering challenging common scenarios that a robot will encounter and should be robust to. Our data was collected with a mobile robotics platform across multiple floors at Northeastern University's ISEC building. Such a multi-floor sequence is typical of commercial office spaces characterized by symmetry across floors and, thus, is prone to perceptual aliasing due to similar floor layouts. The sensor suite comprises seven global shutter cameras, a high-grade MEMS inertial measurement unit (IMU), a ZED stereo camera, and a 128-channel high-resolution lidar. Along with the dataset, we benchmark several SLAM algorithms and highlight the problems faced during the runs, such as perceptual aliasing, visual degradation, and trajectory drift. The benchmarking results indicate that parts of the dataset work well with some algorithms, while other data sections are challenging for even the best SOTA algorithms. The dataset is available at https://github.com/neufieldrobotics/NUFR-M3F.

翻译：即时定位与地图构建（SLAM）的鲁棒性仍是自主系统实际部署中的关键挑战之一。尽管过去25年间SLAM研究取得了显著进展，但诸多最先进（SOTA）算法在真实环境中的可靠运行仍面临困难。研究界普遍认为，我们需要能暴露传感器模态不同失效模式的挑战性真实场景。本文提出了一种新颖的多模态室内SLAM数据集，覆盖了机器人可能遇到的常见挑战场景，并要求其具备鲁棒性。数据由移动机器人平台在东北大学ISEC大楼的多楼层环境中采集完成。这种典型的多楼层序列具有商业办公空间的对称性特征——不同楼层布局相似，因此易因楼层结构相似性产生感知混淆。传感器套件包含七个全局快门相机、一个高等级MEMS惯性测量单元（IMU）、一个ZED立体相机以及一个128通道高分辨率激光雷达。除数据集外，我们还对多种SLAM算法进行了基准测试，并重点阐述了运行过程中遇到的问题，如感知混淆、视觉退化与轨迹漂移。基准测试结果表明，部分数据段对某些算法效果良好，而其他数据段即使对最先进的SOTA算法也构成挑战。该数据集可通过https://github.com/neufieldrobotics/NUFR-M3F获取。