通过时空一致性提升视觉里程计精度，有效解决长期跟踪难题。
Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual Odometry
- 引入时空传播与空间激活模块，增强多帧光流匹配的一致性
- 在ETH3D和KITTI上分别提升77.8%和38.9%的定位精度
- 适合需要高鲁棒性长序列视觉定位的自动驾驶场景
近期视觉里程计（VO）方法通过深度网络预测视频帧间光流显著提升了性能。然而，现有方法仍面临光流匹配噪声大、不一致的问题，难以应对复杂场景与长序列估计。为此，本文提出时空视觉里程计（STVO），一种新型深度网络架构，通过有效利用内在的时空线索，提升多帧光流匹配的准确性和一致性。更精确一致的光流匹配使后续捆绑调整（BA）获得更优位姿估计。STVO包含两项创新组件：1）时间传播模块，利用多帧信息跨相邻帧提取并传播时间线索，保持时间一致性；2）空间激活模块，利用深度图提供的几何先验增强空间一致性，同时过滤过多噪声与错误匹配。STVO在TUM-RGBD、EuRoc MAV、ETH3D和KITTI Odometry基准上均达到当前最优性能，尤其在ETH3D上较之前最佳方法提升77.8%，在KITTI Odometry上提升38.9%。
原文摘要 · Abstract (English)
Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noisy and inconsistent flow matching, making it difficult to handle challenging scenarios and long-sequence estimation. To overcome these challenges, we introduce Spatio-Temporal Visual Odometry (STVO), a novel deep network architecture that effectively leverages inherent spatio-temporal cues to enhance the accuracy and consistency of multi-frame flow matching. With more accurate and consistent flow matching, STVO can achieve better pose estimation through the bundle adjustment (BA). Specifically, STVO introduces two innovative components: 1) the Temporal Propagation Module that utilizes multi-frame information to extract and propagate temporal cues across adjacent frames, maintaining temporal consistency; 2) the Spatial Activation Module that utilizes geometric priors from the depth maps to enhance spatial consistency while filtering out excessive noise and incorrect matches. Our STVO achieves state-of-the-art performance on TUM-RGBD, EuRoc MAV, ETH3D and KITTI Odometry benchmarks. Notably, it improves accuracy by 77.8% on ETH3D benchmark and 38.9% on KITTI Odometry benchmark over the previous best methods.
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