  In this paper, we propose a new visual navigation method based on a single
RGB perspective camera. Using the Visual Teach & Repeat (VT&R) methodology, the
robot acquires a visual trajectory consisting of multiple subgoal images in the
teaching step. In the repeat step, we propose two network architectures, namely
ViewNet and VelocityNet. The combination of the two networks allows the robot
to follow the visual trajectory. ViewNet is trained to generate a future image
based on the current view and the velocity command. The generated future image
is combined with the subgoal image for training VelocityNet. We develop an
offline Model Predictive Control (MPC) policy within VelocityNet with the dual
goals of (1) reducing the difference between current and subgoal images and (2)
ensuring smooth trajectories by mitigating velocity discontinuities. Offline
training conserves computational resources, making it a more suitable option
for scenarios with limited computational capabilities, such as embedded
systems. We validate our experiments in a simulation environment, demonstrating
that our model can effectively minimize the metric error between real and
played trajectories.
