[
    {
        "id": "osp-25673",
        "type": "article-journal",
        "title": "ForVis: An In-Field Dataset and Benchmark for VIO Using Under-Canopy UAV Flights in Forests",
        "author": [
            {
                "family": "Kiani",
                "given": "Arman"
            },
            {
                "family": "Ataei",
                "given": "Masoud"
            },
            {
                "family": "Gyaase",
                "given": "Elvis"
            },
            {
                "family": "Eiyike",
                "given": "Jeffrey"
            },
            {
                "family": "Weiskittel",
                "given": "Aaron"
            },
            {
                "family": "Chakraborty",
                "given": "Prabuddha"
            },
            {
                "family": "Dhiman",
                "given": "Vikas"
            }
        ],
        "URL": "https://omanscience.com/en/articles/forvis-an-in-field-dataset-and-benchmark-for-vio-using-under-canopy-uav-flights-in-forests",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Visual-inertial Simultaneous Localization and Mapping (VI-SLAM) for UAVs remains difficult to evaluate in real forest environments, where motion, illumination changes, repetitive vegetation, and vibration can all affect estimation. We present ForVis, an in-field dataset and benchmark for evaluating VI-SLAM during UAV flight in forest environments. The dataset contains twelve flights across open meadow, above-canopy, and under-canopy conditions in each environment. In total, it provides 563.8s of flight over 1096.8m of trajectory, recorded simultaneously with an Intel RealSense D435i and an OAK-D Pro Wide together with inertial and flight-controller data. We benchmark seven open-source VI-SLAM systems over 504 runs. The results show that sensor choice has a larger effect on trajectory error than the spread between algorithms: all seven methods achieve lower median error on the OAK-D Pro than on the D435i. ForVis is intended to support evaluation of speed, accuracy and robustness for VI-SLAM in challenging forest flight."
    }
]