Visual odometry estimates the motion of a moving camera based on visual
input. Existing methods, mostly focusing on two-view point tracking, often
ignore the rich temporal context in the image sequence, thereby overlooking the
global motion patterns and providing no assessment of the full trajectory
reliability. These shortcomings hinder performance in scenarios with occlusion,
dynamic objects, and low-texture areas. To address these challenges, we present
the Long-term Effective Any Point Tracking (LEAP) module. LEAP innovatively
combines visual, inter-track, and temporal cues with mindfully selected anchors
for dynamic track estimation. Moreover, LEAP's temporal probabilistic
formulation integrates distribution updates into a learnable iterative
refinement module to reason about point-wise uncertainty. Based on these
traits, we develop LEAP-VO, a robust visual odometry system adept at handling
occlusions and dynamic scenes. Our mindful integration showcases a novel
practice by employing long-term point tracking as the front-end. Extensive
experiments demonstrate that the proposed pipeline significantly outperforms
existing baselines across various visual odometry benchmarks.

基于视觉输入，视觉里程计根据图像序列中的丰富时间上下文和选择的关键点，通过 LEAP 模块进行长期有效的任意点跟踪，有效解决遮挡、动态对象和低纹理区域等挑战，从而提供全轨迹可靠性评估，其前端应用了长期点跟踪的新实践方法，实验证明在各种视觉里程计基准中显著优于现有基线。