A Game of Bundle Adjustment -- Learning Efficient Convergence
Amir Belder Refael Vivanti Ayellet Tal
Bundle adjustment is the common way to solve localization and mapping. It is
an iterative process in which a system of non-linear equations is solved using
two optimization methods, weighted by a damping factor. In the classic
approach, the latter is chosen heuristically by the Levenberg-Marquardt
algorithm on each iteration. This might take many iterations, making the
process computationally expensive, which might be harmful to real-time
applications. We propose to replace this heuristic by viewing the problem in a
holistic manner, as a game, and formulating it as a reinforcement-learning
task. We set an environment which solves the non-linear equations and train an
agent to choose the damping factor in a learned manner. We demonstrate that our
approach considerably reduces the number of iterations required to reach the
bundle adjustment's convergence, on both synthetic and real-life scenarios. We
show that this reduction benefits the classic approach and can be integrated
with other bundle adjustment acceleration methods.