Robust Monocular Visual Odometry Via Dual-Paradigm Curriculum Learning

Abstract

Monocular visual odometry (VO) is accurate in controlled settings yet drifts sharply under aggressive motion and sensor noise. We offer a fundamental rethinking of VO robustness as a training-schedule problem rather than an architectural challenge, introducing a novel dual-paradigm curriculum learning framework that operates at both trajectory and loss-component levels. (i) A motion-based curriculum orders trajectories by measured motion complexity. (ii) A hierarchical component curriculum adaptively re-weights optical-flow, pose, and rotation losses via Self-Paced and in-training Reinforcement Learning (RL) schedulers. Integrated into an unmodified DPVO baseline, these strategies cut TartanAir ATE by 33% with only 31% extra training wall-time, and reach baseline accuracy 47% faster (Self-Paced). Without fine-tuning, the same models improve zero-shot performance on EuRoC (-13%), TUM-RGBD (-9%), and ICL-NUIM (-32%). We show that explicit difficulty progression or adaptive loss weighting provides a practical, zero-inference-overhead path to robust monocular VO and could extend to other geometric vision tasks.