# Autonomous Hiking Trail Navigation via Semantic Segmentation and Geometric Analysis

arXiv: https://arxiv.org/abs/2409.15671 · HTML: https://mlsanity.com/paper/2409.15671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [TensorMask: A Foundation for Dense Object Segmentation](https://mlsanity.com/paper/1903.12174/) (2019)
- [Autonomous Exploration Development Environment and the Planning Algorithms](https://mlsanity.com/paper/2110.14573/) (2021)
- [OFFSEG: A Semantic Segmentation Framework For Off-Road Driving](https://mlsanity.com/paper/2103.12417/) (2021)
- [Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation](https://mlsanity.com/paper/1803.02784/) (2018)
- [Design of an Autonomous Precision Pollination Robot](https://mlsanity.com/paper/1808.10010/) (2018)
- [The k-d tree data structure and a proof for neighborhood computation in expected logarithmic time](https://mlsanity.com/paper/1903.04936/) (2019)

## Built on by (most cited first, 1 of 1)

- [SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling](https://mlsanity.com/paper/2608.29315/) (2026)

## Abstract (arXiv)

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.