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Topic

A semi-synthetic approach to creating visual-inertial odometry datasets.

05 November 2024

Sam Schofield

HOW TO APPLY

Institute

University of Canterbury

Time & Place

Thu, 03 Jun 2021 12:00:00 NZST in Rehua 529

 

Abstract

Capturing outdoor visual-inertial datasets is a challenging yet vital aspect of developing robust visual-inertial odometry (VIO) algorithms. A significant hurdle is that high-accuracy-ground-truth systems (e.g., motion capture) are not practical for outdoor use. One solution is to use a ``semi-synthetic" approach that combines rendered images with real IMU data. This approach can produce sequences containing challenging imagery and accurate ground truth but with less simulated data than a fully synthetic sequence. Existing methods record IMU measurements from a visual-inertial system while measuring its trajectory using motion capture, then rendering images along that trajectory. We propose fusing motion capture and IMU measurements to generate a more accurate trajectory for the rendering process. We demonstrate that this improved trajectory results in better consistency between the IMU data and rendered images. Furthermore, we show that VIO  trajectory error is reduced by 79% when evaluated on sequences generated using our approach compared to existing methods.

 

Biography

Sam is a PhD student focusing on improving the robustness of visual odometry in challenging environments. He is also a research assistant for UCDroneLab working on autonomous UAVs.

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