SMapper: 一种用于SLAM基准测试的多模态数据采集平台
SMapper: A Multi-Modal Data Acquisition Platform for SLAM Benchmarking
AI总结：
SMapper是一种用于SLAM基准测试的多模态数据采集平台，通过开放硬件设计和可重复的数据收集，提供高精度的SLAM数据集和基准测试结果，推动SLAM算法的发展和评估。
摘要：
在同时定位与建图（SLAM）和自主导航等领域，研究进展严重依赖于可靠且可重复的多模态数据集的可用性。尽管有几个有影响力的数据显示集推动了这些领域的进步，但它们在传感模态、环境多样性和底层硬件设置的可重复性方面往往存在限制。为了解决这些挑战，本文介绍了一个名为SMapper的新开放硬件、多传感器平台，专门设计用于SLAM研究，但不仅限于此。该设备集成了同步激光雷达、多相机和惯性传感，并通过强大的校准和同步管道确保跨模态的精确时空对齐。其开放且可重复的设计使研究人员能够扩展其能力并在手持和机器人安装的场景中重复实验。为了证明其实用性，我们还发布了SMapper-light，一个公开可用的SLAM数据集，包含代表性的室内外序列。该数据集包括紧密同步的多模态数据和通过离线激光雷达SLAM获得的地面真实轨迹，精度达到亚厘米级，以及密集的3D重建。此外，论文还包含使用SMapper-light数据集在最新激光雷达和视觉SLAM框架上的基准测试结果。通过结合开放硬件设计、可重复的数据收集和全面的基准测试，SMapper为推进SLAM算法开发、评估和可重复性建立了坚实的基。项目的文档，包括源代码、CAD模型和数据集链接，可在https://snt-arg.github.io/smapper_docs公开获取。
英文摘要：
Advancing research in fields such as Simultaneous Localization and Mapping (SLAM) and autonomous navigation critically depends on the availability of reliable and reproducible multimodal datasets. While several influential datasets have driven progress in these domains, they often suffer from limitations in sensing modalities, environmental diversity, and the reproducibility of the underlying hardware setups. To address these challenges, this paper introduces SMapper, a novel open-hardware, multi-sensor platform designed explicitly for, though not limited to, SLAM research. The device integrates synchronized LiDAR, multi-camera, and inertial sensing, supported by a robust calibration and synchronization pipeline that ensures precise spatio-temporal alignment across modalities. Its open and replicable design allows researchers to extend its capabilities and reproduce experiments across both handheld and robot-mounted scenarios. To demonstrate its practicality, we additionally release SMapper-light, a publicly available SLAM dataset containing representative indoor and outdoor sequences. The dataset includes tightly synchronized multimodal data and ground truth trajectories derived from offline LiDAR-based SLAM with sub-centimeter accuracy, alongside dense 3D reconstructions. Furthermore, the paper contains benchmarking results on state-of-the-art LiDAR and visual SLAM frameworks using the SMapper-light dataset. By combining open-hardware design, reproducible data collection, and comprehensive benchmarking, SMapper establishes a robust foundation for advancing SLAM algorithm development, evaluation, and reproducibility. The project's documentation, including source code, CAD models, and dataset links, is publicly available at https://snt-arg.github.io/smapper_docs.
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。
