A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
Open accessAbstract
We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle.
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A deep neural network-based vision recognition system trained on a large dataset performs better than humans at recognising and evaluating forest or mountain trails from a single monocular image.
“The obtained results showed that a deep neural network-based system, trained on a large dataset, performs better than humans.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [10]
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- Authors
- Giusti, A., Guzzi, J., Ciresan, D. C., He, F.-L., Rodriguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., Scaramuzza, D., & Gambardella, L. M.
- Affiliations
- University of Applied Sciences and Arts of Southern Switzerland (CH)
- University of Zurich (CH)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2016-07
- Volume
- 1
- Issue
- 2
- Pages
- 661-667
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2296673577
- Cited by
- 665
- References
- 1
- Citations by year
- 2026: 17 · 2025: 24 · 2024: 28 · 2023: 51 · 2022: 68 · 2021: 96 · 2020: 102 · 2019: 113 · 2018: 97 · 2017: 53 · 2016: 15
- FWCI
- 43.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Swiss National Science Foundation (SNSF)
- National Centre of Competence in Research (NCCR) Robotics
- Supervised Deep/Recurrent Nets (140399)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- deep neural network, mobile robots, quadrotor control, micro air vehicles, visual perception
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