ForVis: An In-Field Dataset and Benchmark for VIO Using Under-Canopy UAV Flights in Forests

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.

Robotics (cs.RO)

arXiv:2609.35482 [cs.RO]

(or arXiv:2609.35482v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.35482 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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