Publication: Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
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Gomez-Ojeda, R., Zhang, Z., Gonzalez-Jimenez, J., & Scaramuzza, D. (2018). Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments. 1–8. https://doi.org/10.1109/ICRA.2018.8462876
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One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors and the inability to perform a successful tracking of interest points because of the bold assumptions in VO, such as brightness constancy. We address this problem from a deep learning perspective, for which we first fine-tune a deep neural network with the purpose of obtaining enhanced representati
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- Gomez-Ojeda, Ruben
- Gonzalez-Jimenez, Javier
- Scaramuzza, Davide
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Gomez-Ojeda, R., Zhang, Z., Gonzalez-Jimenez, J., & Scaramuzza, D. (2018). Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments. 1–8. https://doi.org/10.1109/ICRA.2018.8462876
