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  3. Accurate Natural Trail Detection Using A Combination Of A Deep Neural Network And Dynamic Programming.
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  3. Accurate Natural Trail Detection Using A Combination Of A Deep Neural Network And Dynamic Programming.

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Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming.

Shyam Prasad Adhikari1, Changju Yang2, Krzysztof Slot3

  • 1Division of Electronics Engineering, Chonbuk National University, Jeonju 567-54896, Korea. all.shyam@gmail.com.

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|January 11, 2018

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel method for detecting and following trails in natural environments using a deep neural network (DNN) and dynamic programming. The approach accurately identifies trails even in complex, unstructured terrains.

Keywords:
deep neural networksdynamic programmingtrail followingtrail segmentation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Trail detection in unstructured natural environments is a significant challenge for autonomous systems.
  • Existing methods struggle with the variability and lack of clear features in natural terrains.
  • Deep neural networks (DNNs) show promise for image processing but require refinement for complex tasks like trail following.

Purpose of the Study:

  • To develop and evaluate a robust vision sensor-based system for detecting and following trails in challenging natural environments.
  • To combine the pattern recognition capabilities of DNNs with the optimization power of dynamic programming for improved trail segmentation.
  • To demonstrate the system's effectiveness using real-world data from a head-mounted vision system.

Main Methods:

  • A patch-based deep neural network (DNN) was trained to classify image patches as 'trail' or 'non-trail'.
  • The DNN was adapted into a fully convolutional architecture to generate trail segmentation maps for various image sizes.
  • Dynamic programming was employed to refine the DNN's output, identifying an optimal trail path from the segmentation map.

Main Results:

  • The combined DNN and dynamic programming approach achieved accurate trail detection in complex natural environments.
  • Experimental results validated the system's performance on real-world trail datasets.
  • The method demonstrated robustness despite the inherent ambiguity of trail features in unstructured terrains.

Conclusions:

  • The proposed vision-based system effectively addresses the challenge of trail detection and following in unstructured natural environments.
  • The integration of deep neural networks and dynamic programming offers a powerful solution for robust autonomous navigation.
  • This research contributes a significant advancement in robotic perception for outdoor applications.