Vision‐based trajectory tracking for mobile robots using Mirage pose estimation method

Abstract

Unmanned vehicles are autonomous robotic systems that are fully or partially controlled by an operator remotely from a station. In the last 2 decades, massive amount of advancements have been observed regarding unmanned vehicles for both military and civilian purposes. Today majority of these vehicles require human guidance even for basic missions, thus, minimizing the human intervention on such systems is one of the emerging research topics. To serve this purpose, this study proposes a new trajectory tracking algorithm using M irage pose estimation method. Mirage employs target pixel errors in 2D image plane and analytically calculates the robot's pose in 3D Euclidean space. Therefore, complex computations are not needed and undesirable Euclidean trajectories are avoided since the vehicle pose is directly controlled. We performed both simulations and real experiments to verify the effectiveness of our method. The results show that the proposed method is a feasible alternative for vision-based Euclidean trajectory tracking with high accuracy and low complexity.

A Real-Time Marker-Based Visual Sensor Based on a FPGA and a Soft Core Processor

“…In the second scenario, the marker traveled on a predefined trajectory and the estimation error has been recorded and compared with [31]. Figure 23 shows pose estimation and rotation angle results.…”

“…Figure 23 shows pose estimation and rotation angle results. In [31], the estimation performance was studied with respect to the number of target points (4, 20, 50, 100). However, we estimate the pose using four coplanar target points only.…”

“…However, we estimate the pose using four coplanar target points only. Table 3 shows the comparison of pose estimation error between implemented coplanar Posit algorithm and [31] results. The results shows that our proposed design outperforms the method introduced in [31].…”

“…Mirage pose estimation has been used for robot localization and trajectory tracking. Pose was estimated analytically without iterative solutions [31]. A POSIT algorithm for non-coplanar feature points has been used for UAV localization in [32].…”

An End-to-End Trainable Multi-Column CNN for Scene Recognition in Extremely Changing Environment

“…With the rapid development of machine learning and artificial intelligence, the application of visual robots has attracted wide attention [1,2]. In particular, visual robots are applied in the field of autonomous navigation, that is motivated by promising application in future autonomous driving.…”