BEV-ODOM2: 基于PV-BEV融合与密集光流监督的增强型BEV单目视觉里程计用于地面机器人
BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots
AI总结：
针对现有BEV方法中位姿训练稀疏监督和透视投影信息丢失的问题，提出BEV-ODOM2框架，通过密集BEV光流监督和PV-BEV融合，在四个数据集上实现40%的RTE提升，并支持边缘实时部署。
摘要：
尺度一致的自我运动估计是自主地面机器人的基础。鸟瞰图（BEV）表示通过提供度量尺度的平面工作空间，自然地解决了单目视觉里程计（MVO）的尺度漂移问题，使得6自由度自我运动简化为更鲁棒的3自由度模型。然而，现有的基于BEV的方法存在两个关键限制：仅从位姿训练得到的稀疏监督信号，以及透视到BEV投影过程中的信息丢失。我们提出了BEV-ODOM2，一个增强框架，无需额外标注即可解决这两个限制。我们的方法引入了（1）直接从3自由度位姿真值构建的密集BEV光流监督，用于像素级指导，以及（2）透视视图（PV）-BEV融合，在投影前计算相关体积以保留6自由度运动线索。增强的旋转采样策略进一步在训练中平衡了不同的运动模式。我们在四个不同空间尺度的数据集上进行了评估：KITTI、Oxford、NCLT和我们新收集的ZJH-VO基准。BEV-ODOM2相比之前的BEV方法实现了40%的RTE提升，在NVIDIA Jetson AGX Orin上的实时推理确认了边缘部署的可行性。源代码和ZJH-VO数据集已公开发布，以促进未来研究。
英文摘要：
Scale-consistent ego-motion estimation is fundamental for autonomous ground robots. Bird's-Eye-View (BEV) representation naturally addresses the scale drift problem of monocular visual odometry (MVO) by providing a metric-scaled planar workspace, enabling the simplification of 6-DoF ego-motion to a more robust 3-DoF model. However, existing BEV-based methods suffer from two key limitations: sparse supervision signals from pose-only training, and information loss during perspective-to-BEV projection. We present BEV-ODOM2, an enhanced framework in the BEV-ODOM line that addresses both limitations without supervision modalities beyond the pose ground truth. Our approach introduces (1) pose-derived dense rigid BEV flow supervision, which reparameterizes the 3-DoF pose ground truth into a pixel-level training signal, and (2) Perspective View (PV)-BEV fusion, which computes correlation volumes before projection to retain additional motion cues and help alleviate projection ambiguity. An enhanced rotation sampling strategy further balances diverse motion patterns during training. We evaluate on four datasets with varied spatial scales: KITTI, Oxford, NCLT, and our newly collected ZJH-VO benchmark. BEV-ODOM2 reduces the RTE of BEV-ODOM by 39% on average across the four datasets. It further enables closed-loop navigation on a physical robot with centimeter-level cross-track error. Real-time inference on an NVIDIA Jetson AGX Orin confirms edge deployment feasibility. The code and the ZJH-VO dataset are publicly released to facilitate future research.
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