Adaptive Continuous Visual Odometry from RGB-D Images

Understand

In this paper, we extend the recently developed continuous visual odometry framework for RGB-D cameras to an adaptive framework via online hyperparameter learning.

• We focus on the case of isotropic kernels with a scalar as the length-scale.
• In practice and as expected, the length-scale has remarkable impacts on the performance of the original framework.
• Previously it was handled using a fixed set of conditions within the solver to reduce the length-scale as the algorithm reaches a local minimum.