1 research outputs found
Learned Monocular Depth Priors in Visual-Inertial Initialization
Visual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR
and autonomous robotic systems today, in both academia and industry. However,
these systems are highly sensitive to the initialization of key parameters such
as sensor biases, gravity direction, and metric scale. In practical scenarios
where high-parallax or variable acceleration assumptions are rarely met (e.g.
hovering aerial robot, smartphone AR user not gesticulating with phone),
classical visual-inertial initialization formulations often become
ill-conditioned and/or fail to meaningfully converge. In this paper we target
visual-inertial initialization specifically for these low-excitation scenarios
critical to in-the-wild usage. We propose to circumvent the limitations of
classical visual-inertial structure-from-motion (SfM) initialization by
incorporating a new learning-based measurement as a higher-level input. We
leverage learned monocular depth images (mono-depth) to constrain the relative
depth of features, and upgrade the mono-depth to metric scale by jointly
optimizing for its scale and shift. Our experiments show a significant
improvement in problem conditioning compared to a classical formulation for
visual-inertial initialization, and demonstrate significant accuracy and
robustness improvements relative to the state-of-the-art on public benchmarks,
particularly under motion-restricted scenarios. We further extend this
improvement to implementation within an existing odometry system to illustrate
the impact of our improved initialization method on resulting tracking
trajectories