Autonomous Hiking Trail Navigation via Semantic Segmentation and Geometric Analysis
<|reference_start|>Autonomous Hiking Trail Navigation via Semantic Segmentation and Geometric Analysis: Natural environments pose significant challenges for autonomous robot navigation, particularly due to their unstructured and ever-changing nature. Hiking trails, with their dynamic conditions influenced by weather, vegetation, and human traffic, represent one such challenge. This work introduces a novel approach to autonomous hiking trail navigation that balances trail adherence with the flexibility to adapt to off-trail routes when necessary. The solution is a Traversability Analysis module that integrates semantic data from camera images with geometric information from LiDAR to create a comprehensive understanding of the surrounding terrain. A planner uses this traversability map to navigate safely, adhering to trails while allowing off-trail movement when necessary to avoid on-trail hazards or for safe off-trail shortcuts. The method is evaluated through simulation to determine the balance between semantic and geometric information in traversability estimation. These simulations tested various weights to assess their impact on navigation performance across different trail scenarios. Weights were then validated through field tests at the West Virginia University Core Arboretum, demonstrating the method's effectiveness in a real-world environment.<|reference_end|>
@article{reed2024autonomous,
    title={Autonomous Hiking Trail Navigation via Semantic Segmentation and
  Geometric Analysis},
    author={Camndon Reed, Christopher Tatsch, Jason N. Gross and Yu Gu},
    journal={arXiv preprint arXiv:2409.15671},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15671},
    primaryClass={cs.RO cs.CV eess.IV}
}