Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effectively. In this paper, we propose RLSAC, a novel Reinforcement Learning enhanced SAmple Consensus framework for end-to-end robust estimation. RLSAC employs a graph neural network to utilize both data and memory features to guide exploring directions for sampling the next minimum set. The feedback of downstream tasks serves as the reward for unsupervised training. Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation. In addition, RLSAC integrates a state transition module that encodes both data and memory features. Our experimental results demonstrate that RLSAC can learn from features to gradually explore a better hypothesis. Through analysis, it is apparent that RLSAC can be easily transferred to other sampling consensus-based robust estimation tasks. To the best of our knowledge, RLSAC is also the first method that uses reinforcement learning to sample consensus for end-to-end robust estimation. We release our codes at https://github.com/IRMVLab/RLSAC.

翻译：鲁棒估计是一项至关重要且仍具挑战性的任务，涉及在噪声环境中估计模型参数。尽管传统的基于采样一致性的算法通过多次采样实现鲁棒性，但这些算法无法有效利用数据特征和历史信息。本文提出RLSAC，一种新颖的强化学习增强的样本一致性框架，用于端到端鲁棒估计。RLSAC采用图神经网络同时利用数据和记忆特征，指导探索方向以采样下一个最小集合。下游任务的反馈作为无监督训练的奖励信号。因此，RLSAC能够避免进行微分学习特征及下游任务反馈，实现端到端鲁棒估计。此外，RLSAC集成了一个状态转换模块，该模块编码了数据和记忆特征。实验结果表明，RLSAC能够从特征中学习并逐步探索更优的假设。通过分析可见，RLSAC可轻松迁移至其他基于采样一致性的鲁棒估计任务。据我们所知，RLSAC也是首个使用强化学习实现端到端鲁棒估计样本一致性的方法。我们已在https://github.com/IRMVLab/RLSAC 公开代码。