  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.
