Trajectory Servoing: Image-Based Trajectory Tracking without Absolute Positioning

Navigation systems with real-time needs often employ hierarchical schemes that decompose navigation across multiple spatial and temporal scales. Doing so permits the navigation solution to respond in real-time to novel information gained from sensors, while being guided by the more slowly evolving global path. At the lowest level of the hierarchy lies trajectory tracking to realize the planned paths or synthesized trajectories. In the absence of an absolute reference (such as GPS) and of an accurate map of the environment, there are no external mechanisms to support trajectory tracking. Onboard mechanisms include odometry through proprioceptive sensors (wheel encoders, IMUs, etc.) or visual sensors. Pose estimation from proprioceptive sensors is not observable, thus visual sensors provide the best mechanism to anchor the robot’s pose estimate to external, static position references.Indeed visual odometry (VO) or visual SLAM (V-SLAM) solutions are essential in these circumstances. However, they too experience drift, mostly due to the integrated effects of measurement noise and system latency. Specificly, the feedback rate from multiple sensor (IMU, Camera, etc.) and control loops are impossible to be perfectly matched since each latency varies, therefore, raw IMU data uncorrected by VO may be directily sent to controller and cause tracking deviation [1]. Additionally, the accumulation of noise (eg IMU bias, camera noise, calibration error, etc.) will cause the VO drift [2] and further undermine the trajectory tracking. From the limitation of cost, cameras that are compatible with small robots are easily be affected by Johnson-Nyquist thermal …