Curriculum Learning (CL), drawing inspiration from natural learning patterns observed in humans and animals, employs a systematic approach of gradually introducing increasingly complex training data during model development. Our work applies innovative CL methodologies to address the challenging geometric problem of monocular Visual Odometry (VO) estimation, which is essential for robot navigation in constrained environments. The primary objective of our research is to push the boundaries of current state-of-the-art (SOTA) benchmarks in monocular VO by investigating various curriculum learning strategies. We enhance the end-to-end Deep-Patch-Visual Odometry (DPVO) framework through the integration of novel CL approaches, with the goal of developing more resilient models capable of maintaining high performance across challenging environments and complex motion scenarios. Our research encompasses several distinctive CL strategies. We develop methods to evaluate sample difficulty based on trajectory motion characteristics, implement sophisticated adaptive scheduling through self-paced weighted loss mechanisms, and utilize reinforcement learning agents for dynamic adjustment of training emphasis. Through comprehensive evaluation on the real-world TartanAir dataset, our Curriculum Learning-based Deep-Patch-Visual Odometry (CL-DPVO) demonstrates superior performance compared to existing SOTA methods, including both feature-based and learning-based VO approaches. The results validate the effectiveness of integrating curriculum learning principles into visual odometry systems.

翻译：课程学习借鉴人类和动物自然学习模式的启发，采用在模型开发过程中逐步引入日益复杂训练数据的系统化方法。本研究应用创新的课程学习方法来解决单目视觉里程计这一具有挑战性的几何估计问题，该技术对于机器人在受限环境中的导航至关重要。本研究的主要目标是通过探索多种课程学习策略，突破当前单目视觉里程计领域最先进基准的性能边界。我们通过整合新颖的课程学习方法，改进了端到端的深度补丁视觉里程计框架，旨在开发更具韧性的模型，使其能够在挑战性环境和复杂运动场景中保持高性能。我们的研究涵盖多种特色课程学习策略：开发了基于轨迹运动特征评估样本难度的方法，通过自定进度加权损失机制实现精细的自适应调度，并利用强化学习智能体动态调整训练重点。通过在真实世界TartanAir数据集上的综合评估，我们基于课程学习的深度补丁视觉里程计相较于现有最先进方法（包括基于特征和基于学习的视觉里程计方法）展现出更优越的性能。实验结果验证了将课程学习原理融入视觉里程计系统的有效性。