Technology
Visual-inertial navigation done right
Whether you challenge is to track the movement of vehicles, people or autonomous things—you will need to understand the device's relative location in the surrounding 3D environment.
While computer vision can help solve these challenges, it alone does not provide knowledge of absolute scale nor reliable performance in real-life environments. Varying lighting conditions, large number of other moving objects, or feature-poor textures can be challenging to handle. This is why information fusion of visual and inertial sensors—two orthogonal information sources—is crucial.
Our methods are learning-based and adapt to uncertainties in the observed data. We do principled probabilistic inference, where both visual and inertial data contribute as equal parties. Our unique approach is built from scratch using first-principles and implemented using state-of-the-art computer vision and machine learning methods.
ROBUST
Fusion gives robustness against camera occlusions, varying lighting conditions, and feature-poor visual environments.
ACCURATE
Error in position less than 1% per covered distance in odometery alone. With SLAM, we reach near zero-drift when revisiting previous locations.
CROSS-PLATFORM
Our C++ codebase can be built for various platforms ranging from smartphones to drones. The methods are configurable to run on different hardware setups.
