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.

翻译：光束法平差是解决定位与建图的常用方法。它是一个迭代过程，通过使用两种优化方法（由阻尼因子加权）来解算非线性方程组。在经典方法中，该阻尼因子由列文伯格-马夸尔特算法在每次迭代中启发式地选择。这可能需要多次迭代，导致计算开销较大，从而可能对实时应用造成不利影响。我们提出从全局视角将这一问题视为一场博弈，并将其形式化为强化学习任务，从而替代这种启发式方法。我们构建了一个解算非线性方程的环境，并训练智能体以学习方式选择阻尼因子。实验证明，在合成场景与真实场景中，我们的方法显著减少了光束法平差达到收敛所需的迭代次数。我们表明，这种减少对经典方法有益，并可与其它光束法平差加速方法集成。