PhD - Efficient and Transferable Deep Visual Odometry

Position: PhD Candidate - Efficient and Transferable Deep Visual Odometry
Organisation/Company Fondazione Bruno Kessler Research Field Other Researcher Profile Other Profession Positions PhD Positions Final date to receive applications 17 Jun 2026 - 23:59 (Europe/Rome) Country Italy Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

Offer Description
Traditional visual odometry (VO) methods are typically classified into feature-based and appearance-based approaches (also known as direct methods). In feature-based methods, the problem may be formulated as a windowed bundle adjustment (WBA) – or solved without it – minimizing the reprojection error. In direct methods, the objective is to define a cost function that estimates the camera pose by minimizing the photometric error across pixels.

Recently, deep visual odometry (DVO) approaches arise and they can be divided into modular and end-to‑to‑end methods. In modular approaches, DL is introduced into specific VO sub‑tasks (e.g. feature extraction, feature matching, optical flow estimation, depth estimation or semantic understanding), where neural networks are highly specialized while the rest of the pipeline relies on classical geometry-based solutions. End‑to‑end DL solutions, where all tasks are learning-based, often suffer from poor generalization due to limitations in training datasets and typically incur high computational costs.

The PhD research should investigate how deep learning could support large-scale VO applications in real-world environments, considering indoor/outdoor complex scenarios, featuring textureless environments and illumination changes.

The expected developments and outcomes of the PhD include:

Identifying the optimal balance between accuracy and processing time when choosing between a modular, component‑wise integration and an end‑to‑end solution, while accounting for generalization limitations

Developing methods for computational compression (e.g., neural network pruning, quantization, or distillation)

Addressing the issue of poor generalization in deep learning by favouring local formulations (such as keypoint extraction and matching) that are inherently more transferable across environments

Leveraging the retraining capabilities offered by the simulated environment of the ESA VAIPOSA project and studying how models trained in simulation can be effectively generalized to real-world datasets.

The ideal candidate should have an attitude towards problem-solving using programming environments, a strong background in AI and knowledge of robotics problems.

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