George Chustz

Peng Jiang, Kasi Viswanath, Akhil Nagariya et al.

The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. Existing off-road datasets often lack sensor diversity and exclude vital modalities like thermal and radar that are critical for operation in degraded conditions (e.g., low visibility or adverse weather). To address these gaps, we introduce a large-scale multimodal off-road dataset with six complementary sensor modalities, along with semantic annotations and GPS traces, to support tasks such as semantic segmentation, object detection, and SLAM. The diverse environmental conditions represented in the dataset present significant real-world challenges, which provide opportunities to develop more robust solutions to support the continued advancement of field robotics, autonomous exploration, and perception systems in natural environments. The dataset can be downloaded at: https://www.unmannedlab.org/the-great-outdoors-dataset/

George Chustz, Srikanth Saripalli

The development and implementation of visual-inertial odometry (VIO) has focused on structured environments, but interest in localization in off-road environments is growing. In this paper, we present the RELLIS Off-road Odometry Analysis Dataset (ROOAD) which provides high-quality, time-synchronized off-road monocular visual-inertial data sequences to further the development of related research. We evaluated the dataset on two state-of-the-art VIO algorithms, (1) Open-VINS and (2) VINS-Fusion. Our findings indicate that both algorithms perform 2 to 30 times worse on the ROOAD dataset compared to their performance in structured environments. Furthermore, OpenVINS has better tracking stability and real-time performance than VINS-Fusion in the off-road environment, while VINS-Fusion outperformed OpenVINS in tracking accuracy in several data sequences. Since the camera-IMU calibration tool from Kalibr toolkit is used extensively in this work, we have included several calibration data sequences. Our hand measurements show Kalibr's tool achieved +/-1 degree for orientation error and +/-1 mm at best (x- and y-axis) and +/-10 mm (z-axis) at worse for position error in the camera frame between the camera and IMU. This novel dataset provides a new set of scenarios for researchers to design and test their localization algorithms on, as well as critical insights in the current performance of VIO in off-road environments. ROOAD Dataset: github.com/unmannedlab/ROOAD