On Visuo-Inertial Fusion for Robot Pose Estimation Using Hierarchical Fuzzy Systems

Feedback motion control of a spatial double pendulum manipulator relying on swept laser based pose estimation

“…Similarly to other freely moving systems, the fusion of the data coming from the several sensors, which typically have various frequencies, is obtained by the Kalman-Filter. [19][20][21] Summing up, we may conclude that the variety of spatial pose estimation techniques is wide and the related software and hardware solutions have been continuously developing. The data processing usually computationally demanding because of the solution of geometric equation sets, image processing and data fusion.…”

“…The present study is carried out by using the pendulum-like manipulator called ACROBOTER, [9,10,12,20] which is structurally similar to a crane and its working unit swings freely in the cubic space. Consequently, its pose estimation and position control are crucial questions.…”

“…Our second goal is to demonstrate via experiments that the HTC Vive can be applied in the feedback loop of a motion controller of an indoor pendulum-like robot. For these purposes, an underactuated indoor pendulum-like robot called ACROBOTER [9,10,12,20] is equipped with the HTC Vive sensor system. [23][24][25] The concept of the pose estimation of the ACROBOTER robot based on the HTC Vive pose sensor has been proposed recently in.…”

Heterogeneous Multi-Sensor Fusion for 2d and 3d Pose Estimation

“…Even though fuzzy techniques in this application have been mostly employed for sensor fusion, their application in the sensor related area is not limited to just fusion. Some researchers have employed fuzzy systems at different levels for noise reduction and smoothing [44,45]. The work of Kyriakoulis et al [45] is a good example of this type of application where the authors employed a three level fuzzy system to create a hierarchical pose estimation algorithm using an IMU and a visual camera.…”

“…Some researchers have employed fuzzy systems at different levels for noise reduction and smoothing [44,45]. The work of Kyriakoulis et al [45] is a good example of this type of application where the authors employed a three level fuzzy system to create a hierarchical pose estimation algorithm using an IMU and a visual camera. The first level is dedicated to the IMU noise reduction, the second level fuses the data from the first level and the data obtained from the camera, and the third level smooths the output of the prior level using the previous system state.…”