Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Efficient Learning-aided Visual Inertial Odometry
Online learning-aided visual inertial odometry for robust state estimation
Keywords: Robotics, Computer Vision
Recent works have shown that deep learning (DL) techniques are beneficial for visual inertial odometry (VIO).
Different ways to include DL in VIO have been proposed: end-to-end learning from images to poses, replacing one/more block/-s of a standard VIO pipeline with learning-based solutions, and include learning in a model-based VIO block.
The project will start with a study of the current literature on learning-based VIO/SLAM algorithms and an evaluation of how/where/when DL is beneficial for VIO/SLAM.
We will use the results of this evaluation to enhance a current state-of-the-art VIO pipeline with DL, focusing our attention on algorithm efficiency at inference time.
The developed learning-aided VIO pipeline will be compared to existing state-of-the-art model-based algorithms, with focus on robustness, and deployed on embedded platforms (Nvidia Jetson TX2 or Xavier).
Recent works have shown that deep learning (DL) techniques are beneficial for visual inertial odometry (VIO). Different ways to include DL in VIO have been proposed: end-to-end learning from images to poses, replacing one/more block/-s of a standard VIO pipeline with learning-based solutions, and include learning in a model-based VIO block. The project will start with a study of the current literature on learning-based VIO/SLAM algorithms and an evaluation of how/where/when DL is beneficial for VIO/SLAM. We will use the results of this evaluation to enhance a current state-of-the-art VIO pipeline with DL, focusing our attention on algorithm efficiency at inference time. The developed learning-aided VIO pipeline will be compared to existing state-of-the-art model-based algorithms, with focus on robustness, and deployed on embedded platforms (Nvidia Jetson TX2 or Xavier).
Enhance standard VIO algorithms with DL techniques to improve robustness.
Benchmark the proposed algorithm against existing state-of-the-art standard VIO algorithms.
Deploy the proposed algorithm on embedded platforms.
We look for students with strong computer vision background and familiar with common software tools used in DL (for example, PyTorch or TensorFlow).
Enhance standard VIO algorithms with DL techniques to improve robustness. Benchmark the proposed algorithm against existing state-of-the-art standard VIO algorithms. Deploy the proposed algorithm on embedded platforms. We look for students with strong computer vision background and familiar with common software tools used in DL (for example, PyTorch or TensorFlow).
Giovanni Cioffi (cioffi@ifi.uzh.ch), Manasi Muglikar (muglikar@ifi.uzh.ch)
Giovanni Cioffi (cioffi@ifi.uzh.ch), Manasi Muglikar (muglikar@ifi.uzh.ch)
