室内SLAM的挑战：用于SLAM评估的多模态多层数据集
Challenges of Indoor SLAM: A multi-modal multi-floor dataset for SLAM evaluation
AI总结：
针对现有SLAM算法在真实室内场景鲁棒性不足的问题，本文发布了含多传感器、多层办公场景的NUFR-M3F数据集及算法基准测试，揭示了感知混叠等典型挑战，为SLAM算法评估提供支撑。
摘要：
同步定位与建图（Simultaneous Localization and Mapping, SLAM）的鲁棒性仍是自主系统实际部署的核心挑战之一。过去25年间SLAM研究取得了显著进展，但许多当前最优（state-of-the-art, SOTA）算法在真实环境中仍难以可靠运行。研究界已达成普遍共识：我们需要更具挑战性的真实场景，以暴露不同感知模态的各类失效模式。\n本文提出了一个全新的多模态室内SLAM数据集，覆盖机器人会遇到且需具备鲁棒性的挑战性常见场景。数据由移动机器人平台在东北大学ISEC大楼的多个楼层采集得到。这类多层序列是商业办公空间的典型场景，其特点是楼层间存在对称性，因此易因相似的楼层布局产生感知混叠。\n传感器套件包含7台全局快门相机、1个高精度MEMS惯性测量单元（inertial measurement unit, IMU）、1台ZED立体相机以及1个128通道高分辨率激光雷达。随数据集一同发布的还有我们对多种SLAM算法的基准测试结果，重点指出了运行过程中遇到的问题，如感知混叠、视觉退化和轨迹漂移。基准测试结果表明，数据集的部分场景适配部分算法，而其他数据片段即便对性能最优的SOTA算法也颇具挑战性。该数据集可在https://github.com/neufieldrobotics/NUFR-M3F 获取。
英文摘要：
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.
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。
