SMapper: A Multi-Modal Data Acquisition Platform for SLAM Benchmarking
Pedro Miguel Bastos Soares, Ali Tourani, Miguel Fernandez-Cortizas, Asier Bikandi-Noya, Holger Voos, Jose Luis Sanchez-Lopez
TL;DR
This work tackles the need for reliable, reproducible SLAM datasets by introducing SMapper, an open-hardware, multi-sensor platform that tightly synchronizes LiDAR, cameras, and IMU data with an IMU-centric calibration and a versatile synchronization pipeline. It releases SMapper-light, a multimodal dataset with ground-truth trajectories generated by offline LiDAR SLAM to enable robust benchmarking of visual, visual–inertial, and LiDAR-based SLAM methods, including dense 3D reconstructions and sub-centimeter accuracy ground truth ($<3\mathrm{cm}$). The authors also provide open-source calibration tooling (Kalibr-based manual and smapper_toolbox automated) and a transparent hardware/software design to maximize reproducibility and extensibility. Benchmarking against state-of-the-art baselines demonstrates practical utility across indoor and outdoor scenarios, validating SMapper as a solid foundation for SLAM algorithm development and cross-domain benchmarking. The work portends broad impact by bridging hardware openness with rigorous multimodal evaluation and reproducible data collection.
Abstract
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.