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Combining multiple sensors enables a robot to maximize its perceptualawareness of environments and enhance its robustness to external disturbance,crucial to robotic navigation. This paper proposes the FusionPortablebenchmark, a complete multi-sensor dataset with a diverse set of sequences formobile robots. This paper presents three contributions. We first advance aportable and versatile multi-sensor suite that offers rich sensorymeasurements: 10Hz LiDAR point clouds, 20Hz stereo frame images, high-rate andasynchronous events from stereo event cameras, 200Hz inertial readings from anIMU, and 10Hz GPS signal. Sensors are already temporally synchronized inhardware. This device is lightweight, self-contained, and has plug-and-playsupport for mobile robots. Second, we construct a dataset by collecting 17sequences that cover a variety of environments on the campus by exploitingmultiple robot platforms for data collection. Some sequences are challenging toexisting SLAM algorithms. Third, we provide ground truth for the decouplelocalization and mapping performance evaluation. We additionally evaluatestate-of-the-art SLAM approaches and identify their limitations. The dataset,consisting of raw sensor easurements, ground truth, calibration data, andevaluated algorithms, will be released:https://ram-lab.com/file/site/multi-sensor-dataset.

FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms