Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses

Understand

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.