A Game of Bundle Adjustment -- Learning Efficient Convergence

• URL: http://arxiv.org/abs/2308.13270v1
• Date: Fri, 25 Aug 2023 09:44:45 GMT
• Title: A Game of Bundle Adjustment -- Learning Efficient Convergence
• Authors: Amir Belder, Refael Vivanti, Ayellet Tal
• Abstract summary: We show how to reduce the number of iterations required to reach the bundle adjustment's convergence. We show that this reduction benefits the classic approach and can be integrated with other bundle adjustment acceleration methods.
• Score: 11.19540223578237
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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