融合视觉与激光信息，实现智能徒步导航。
Autonomous Hiking Trail Navigation via Semantic Segmentation and Geometric Analysis
- 用相机和激光雷达融合语义与几何数据
- 仿真测试验证不同权重下的导航性能
- 野外实测证明可安全应对复杂地形
自然环境对自主机器人导航构成重大挑战，尤其因其非结构化和动态变化的特性。徒步小径受天气、植被和人为活动影响，条件多变，是典型难题。本文提出一种新型自主徒步路径导航方法，兼顾沿路行驶与必要时偏离路径的灵活性。核心为一个可通行性分析模块，将摄像头图像的语义信息与激光雷达的几何信息融合，构建对周围地形的全面理解。规划器基于此可通行性地图进行导航，在遵循路径的同时，可灵活偏离以避开障碍或选择安全的非路径捷径。通过仿真评估语义与几何信息在可通行性估计中的权衡，测试了多种权重组合对不同路径场景下导航表现的影响。随后在西弗吉尼亚大学核心植物园进行实地测试，验证了该方法在真实环境中的有效性。
原文摘要 · Abstract (English)
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.
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