图像帧动态目标分割与基于雷达图像融合的自我运动估计
Image Frame Dynamic Object Segmentation and Ego Motion Estimation using Radar Image Fusion
浏览论文内容
中文总结 AI 辅助
针对自动驾驶中动态目标分割与自我运动估计的耦合问题，提出Radar-Dot框架，利用雷达多普勒测量、线性约束和光流融合，在nuScenes上实现动态IoU 20.24%和F1 33.67%，并改善自我速度估计。
中文摘要 AI 辅助
动态目标分割与自我运动估计是自动驾驶中紧密耦合的问题，因为准确的自我运动估计通常需要静态场景观测，而识别静态观测又需要了解自我运动。我们提出了Radar-Dot，一种利用雷达多普勒测量来解决这种耦合的雷达-RGB框架。首先使用雷达回波通过线性多普勒约束估计自我速度，并采用基于残差的静态/动态分割和鲁棒估计来减少运动物体的影响。然后将估计的运动与度量深度和稠密光流相结合，以识别其观测运动与刚性场景运动不一致的图像区域。在10个nuScenes场景（nuscenes-mini的一部分）上的实验表明，所得到的几何流程在394帧对上实现了20.24%的动态IoU和33.67%的F1分数，而基于雷达的静态点滤波相比使用所有雷达回波，改善了自我速度估计。这些结果证明了雷达作为一种模态在联合改善自我运动估计和动态目标分割方面的潜力。
英文摘要
Dynamic object segmentation and ego-motion estimation are closely coupled problems in autonomous driving, as accurate ego-motion estimation typically requires static scene observations, while identifying static observations requires knowledge of the ego motion. We present Radar-Dot, a radar--RGB framework that exploits radar Doppler measurements to address this coupling. Radar returns are first used to estimate ego velocity through a linear Doppler constraint, with residual-based static/dynamic segmentation and robust estimation used to reduce the influence of moving objects. The estimated motion is then combined with metric depth and dense optical flow to identify image regions whose observed motion is inconsistent with the rigid scene motion. Experiments on 10 nuScenes scenes (part of nuscenes-mini) demonstrate that the resulting geometric pipeline achieves 20.24% dynamic IoU and 33.67% F1-score over 394 frame pairs, while radar-based static-point filtering improves ego-velocity estimation compared with using all radar returns. These results demonstrate the potential of radar as a modality for jointly improving ego-motion estimation and dynamic object segmentation.
