AutoLoop：通过智能体课程学习实现快速视觉SLAM微调
AutoLoop: Fast Visual SLAM Fine-tuning through Agentic Curriculum Learning
AI总结：
针对视觉SLAM系统回环处理效率与鲁棒性难平衡的问题，提出结合DDPG智能体动态调权与离线预计算回环对的自动化课程学习方法AutoLoop，可将训练时间减少一个数量级且性能更优。
摘要：
当前视觉SLAM系统在平衡计算效率与鲁棒回环处理方面面临重大挑战。传统方法需要精细的人工调优，且会产生大量计算开销；而基于学习的方法要么缺乏显式回环检测能力，要么通过计算成本高昂的方式实现回环功能。我们提出AutoLoop，这是一种将自动化课程学习与视觉SLAM系统高效微调相结合的新方法。该方法采用DDPG（深度确定性策略梯度）智能体在训练过程中动态调整回环权重，无需手动搜索超参数，同时大幅减少了所需的训练步数。该方法离线预计算潜在回环对，并通过智能体引导的课程加以利用，使模型能够高效适配新场景。在TartanAir数据集上进行训练、并在KITTI、EuRoC、ICL-NUIM和TUM RGB-D等多个基准上验证的实验表明，AutoLoop取得了相当或更优的性能，同时与传统方法相比训练时间减少了一个数量级。AutoLoop为视觉SLAM系统的快速适配提供了实用解决方案，实现了传统上需要多次人工迭代的权重调优过程的自动化。我们的结果表明，这种自动化课程策略不仅能加速训练，还能在多样环境条件下保持或提升模型性能。
英文摘要：
Current visual SLAM systems face significant challenges in balancing computational efficiency with robust loop closure handling. Traditional approaches require careful manual tuning and incur substantial computational overhead, while learning-based methods either lack explicit loop closure capabilities or implement them through computationally expensive methods. We present AutoLoop, a novel approach that combines automated curriculum learning with efficient fine-tuning for visual SLAM systems. Our method employs a DDPG (Deep Deterministic Policy Gradient) agent to dynamically adjust loop closure weights during training, eliminating the need for manual hyperparameter search while significantly reducing the required training steps. The approach pre-computes potential loop closure pairs offline and leverages them through an agent-guided curriculum, allowing the model to adapt efficiently to new scenarios. Experiments conducted on TartanAir for training and validated across multiple benchmarks including KITTI, EuRoC, ICL-NUIM and TUM RGB-D demonstrate that AutoLoop achieves comparable or superior performance while reducing training time by an order of magnitude compared to traditional approaches. AutoLoop provides a practical solution for rapid adaptation of visual SLAM systems, automating the weight tuning process that traditionally requires multiple manual iterations. Our results show that this automated curriculum strategy not only accelerates training but also maintains or improves the model's performance across diverse environmental conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
