Successfully navigating these environments requires leveraging both visual and geometric features effectively. Modeling tire-terrain interactions and vehicle dynamics across diverse off-road conditions is a complex task. Even with accurate models, tuning the planning algorithm to navigate safely across different terrains demands extensive time and expertise. In our research, we introduce a demonstration-based local planning algorithm that bypasses the need for directly modeling these intricate dynamic interactions. Instead, it learns navigation preferences from human driving data, demonstrating the ability to adapt these learned behaviors from simulations to real vehicles with minimal manual adjustments. Our approach uses utility functions to directly extract key features from segmented images and learns human driving behaviour using demonstration data. This approach diverges from traditional methods, which typically require either extensive labeled data for end-to-end learning or precise sensor calibration and global mapping in classical robotics approaches.

The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. This dataset provides the most comprehensive set of data modalities and annotations compared to existing off-road datasets. In total, the GO dataset includes six unique sensor types with high-quality 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 that 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/