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.

翻译：本文提出一种基于单目RGB透视角相机的新视觉导航方法。该方法采用视觉“示教与重复”（VT&R）技术，在示教阶段由机器人采集包含多个子目标图像的视觉轨迹。在重复阶段，我们提出两种网络架构：视图网络（ViewNet）与速度网络（VelocityNet）。二者结合使机器人能够沿视觉轨迹行进。ViewNet经训练后，可根据当前视角与速度指令生成未来图像，该未来图像与子目标图像共同用于VelocityNet的训练。我们在VelocityNet内开发了离线模型预测控制（MPC）策略，其双重目标为：（1）减少当前图像与子目标图像之间的差异；（2）通过抑制速度突变确保轨迹平滑性。离线训练可节省计算资源，更适用于嵌入式系统等算力受限场景。我们在仿真环境中验证了实验，结果表明该模型能有效最小化实际轨迹与回放轨迹之间的度量误差。