The reduced cost and computational and calibration requirements of monocular cameras make them ideal positioning sensors for mobile robots, albeit at the expense of any meaningful depth measurement. Solutions proposed by some scholars to this localization problem involve fusing pose estimates from convolutional neural networks (CNNs) with pose estimates from geometric constraints on motion to generate accurate predictions of robot trajectories. However, the distribution of attitude estimation based on CNN is not uniform, resulting in certain translation problems in the prediction of robot trajectories. This paper proposes improving these CNN-based pose estimates by propagating a SE(3) uniform distribution driven by a particle filter. The particles utilize the same motion model used by the CNN, while updating their weights using CNN-based estimates. The results show that while the rotational component of pose estimation does not consistently improve relative to CNN-based estimation, the translational component is significantly more accurate. This factor combined with the superior smoothness of the filtered trajectories shows that the use of particle filters significantly improves the performance of CNN-based localization algorithms.

翻译：单目相机因其成本低、计算和标定要求低而成为移动机器人的理想定位传感器，但代价是缺乏有意义的深度测量。针对这一定位问题，部分学者提出的解决方案包括将卷积神经网络（CNN）的姿态估计与运动几何约束的姿态估计相融合，以生成机器人轨迹的准确预测。然而，基于CNN的姿态估计分布不均匀，导致机器人轨迹预测存在一定的平移问题。本文提出通过传播由粒子滤波驱动的SE(3)均匀分布来改进这些基于CNN的姿态估计。粒子利用与CNN相同的运动模型，同时使用基于CNN的估计更新其权重。结果表明，尽管姿态估计的旋转分量相对于基于CNN的估计并未持续改进，但平移分量的准确性显著提高。这一因素与滤波轨迹的优异平滑性相结合，表明粒子滤波显著提升了基于CNN的定位算法性能。