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/

LiDAR semantic segmentation frameworks predominantly use geometry-based features to differentiate objects within a scan. These methods excel with clear boundaries but struggle in ambiguous environments, especially off-road. Recent 3D segmentation advances use raw LiDAR intensity for better prediction accuracy. Nonetheless, existing models face challenges in relating raw intensity to distance, angle, reflectivity, and atmospheric conditions. Building on prior work Viswanath et al. (2024), we examine the benefits of calibrated intensity (reflectivity) in learning-based LiDAR segmentation. Adding reflectivity as input enhances data representation, resulting in a 4% mIoU improvement on the Rellis-3d off-road dataset. We also explore calibrated intensity benefits for urban segmentation (SemanticKITTI) and cross-sensor adaptation. Testing the Segment Anything Model (SAM) Kirillov et al. (2023) with reflectivity led to improved masks for LiDAR images.

LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning. Learning-based LiDAR semantic segmentation utilizes machine learning techniques to automatically classify objects and regions in LiDAR point clouds. Learning-based models struggle in off-road environments due to the presence of diverse objects with varying colors, textures, and undefined boundaries, which can lead to difficulties in accurately classifying and segmenting objects using traditional geometric-based features. In this paper, we address this problem by harnessing the LiDAR intensity parameter to enhance object segmentation in off-road environments. Our approach was evaluated in the RELLIS-3D data set and yielded promising results as a preliminary analysis with improved mIoU for classes “puddle” and “grass” compared to more complex deep learning-based benchmarks ( https://github.com/MOONLABIISERB/lidar-intensity-predictor/tree/main ). The methodology was evaluated for compatibility across both Velodyne and Ouster LiDAR systems, assuring its cross-platform applicability. This analysis advocates for the incorporation of calibrated intensity as a supplementary input, aiming to enhance the prediction accuracy of learning based semantic segmentation frameworks.

Off-road image semantic segmentation is challenging due to the presence of uneven terrain, unstructured class boundaries, irregular features and strong textures. These aspects affect the vehicle perception. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a framework for off-road semantic segmentation (OFFSEG) that involves (i) a pooled class semantic segmentation with four classes (sky, traversable region, non-traversable region and obstacle) using state-of-the-art deep learning architectures (ii) a color segmentation methodology to segment out specific sub-classes (grass, puddle, dirt, gravel, etc.) from the traversable region for better scene understanding. The evaluation of the framework is carried out on two off-road driving datasets, namely, RELLIS-3D and RUGD. We have also tested the proposed framework on IISERB campus data. The results show that OFFSEG achieves good performance and also provides detailed information on the traversable region.