Enabling Robust, Real-Time Verification of Vision-Based Navigation through View Synthesis
This addresses the slow and difficult setup of traditional validation methods for vision-based navigation systems, though it appears incremental as an enhancement to existing validation approaches.
The paper tackles the problem of validating vision-based navigation algorithms by introducing VISY-REVE, a pipeline that augments sparse image datasets with synthesized views at novel poses in real-time, achieving continuous trajectories from existing data. It also proposes the Boresight Deviation Distance metric for better view synthesis, enabling increased dataset density.
This work introduces VISY-REVE: a novel pipeline to validate image processing algorithms for Vision-Based Navigation. Traditional validation methods such as synthetic rendering or robotic testbed acquisition suffer from difficult setup and slow runtime. Instead, we propose augmenting image datasets in real-time with synthesized views at novel poses. This approach creates continuous trajectories from sparse, pre-existing datasets in open or closed-loop. In addition, we introduce a new distance metric between camera poses, the Boresight Deviation Distance, which is better suited for view synthesis than existing metrics. Using it, a method for increasing the density of image datasets is developed.