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Updated: Jul 23, 2025

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An embarrassingly simple approach for visual navigation of forest environments.

Chaoyue Niu1, Callum Newlands1, Klaus-Peter Zauner1

  • 1School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom.

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|July 14, 2023
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Summary

This study introduces a low-cost navigation system for small forest robots using a lightweight neural network to predict terrain depth. The system successfully navigated over 750m of challenging forest terrain in real-world trials.

Keywords:
compliant obstaclesdepth predictionforest simulationlow-cost sensorslow-viewpoint forest navigationoff-road navigationsmall-sized roverssparse swarms

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Area of Science:

  • Field Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Forest navigation presents significant challenges for field robots due to unknown terrains, varied obstacles, and changing environmental conditions.
  • Small, low-viewpoint robots face occlusion issues, unlike larger, more expensive systems typically used in off-road navigation research.
  • Existing navigation systems often rely on costly sensors, limiting accessibility for small-scale robotic applications.

Purpose of the Study:

  • To design and evaluate a low-cost, vision-based navigation system for small-sized rovers in forest environments.
  • To enable autonomous or semi-autonomous navigation of portable robots through complex natural terrains.
  • To address the limitations of low viewpoint and sensor occlusion in small robotic platforms.

Main Methods:

  • A lightweight convolutional neural network (CNN) was employed to predict depth images from low-viewpoint RGB camera input.
  • A coarse-grained navigation algorithm processed predicted depth information to identify traversable paths and avoid obstacles.
  • The system was tested using a Raspberry Pi on a manually pushed mobile platform in both high-fidelity simulations and real-world field trials.

Main Results:

  • The navigation system successfully guided the mobile platform over 750 meters of diverse forest terrain, including shrubs, fallen trees, and uneven ground.
  • Navigation was achieved using minimal input (16x16 depth prediction from 32x32 RGB image) and demonstrated robustness across various weather and lighting conditions.
  • The algorithm showed resilience to camera pitch angle variations, motion blur, low light, and high-contrast lighting.

Conclusions:

  • A low-cost, vision-based navigation system is feasible for small forest rovers, overcoming challenges of low viewpoint and complex terrain.
  • The developed system offers a practical solution for robotic exploration and monitoring in unstructured forest environments.
  • The approach demonstrates the potential for resource-constrained robots to navigate challenging natural landscapes effectively.