IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation

Shi, Yutong; Zhang, Yongbo; Li, Ziteng; Yuan, Shangwu; Zhu, Shihao

doi:10.3390/s23156700

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…However, camerabased Mocap systems face challenges such as occlusions in certain camera views [23]. To overcome these problems, some researchers have designed hybrid systems that combine different Mocap technologies [33,34] to improve accuracy and reduce camera occlusions. As previously mentioned, autonomous control of drones requires high-frequency position and orientation information, so the structure should be as simple and efficient as possible.…”

Section: Motion Capture Methodsmentioning

confidence: 99%

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Wang,

Chen,

et al. 2024

Drones

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Fast and accurate pose estimation is essential for the local motion control of robots such as drones. At present, camera-based motion capture (Mocap) systems are mostly used by robots. However, this kind of Mocap system is easily affected by light noise and camera occlusion, and the cost of common commercial Mocap systems is high. To address these challenges, we propose Easy Rocap, a low-cost, open-source robot motion capture system, which can quickly and robustly capture the accurate position and orientation of the robot. Firstly, based on training a real-time object detector, an object-filtering algorithm using class and confidence is designed to eliminate false detections. Secondly, multiple-object tracking (MOT) is applied to maintain the continuity of the trajectories, and the epipolar constraint is applied to multi-view correspondences. Finally, the calibrated multi-view cameras are used to calculate the 3D coordinates of the markers and effectively estimate the 3D pose of the target robot. Our system takes in real-time multi-camera data streams, making it easy to integrate into the robot system. In the simulation scenario experiment, the average position estimation error of the method is less than 0.008 m, and the average orientation error is less than 0.65 degrees. In the real scenario experiment, we compared the localization results of our method with the advanced LiDAR-Inertial Simultaneous Localization and Mapping (SLAM) algorithm. According to the experimental results, SLAM generates drifts during turns, while our method can overcome the drifts and accumulated errors of SLAM, making the trajectory more stable and accurate. In addition, the pose estimation speed of our system can reach 30 Hz.

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Section: Motion Capture Methodsmentioning

confidence: 99%

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Wang,

Chen,

et al. 2024

Drones

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“…J. T et al proposed a novel algorithm for motion estimation that could reduce the drift in IMU sensors. This algorithm employed a Kalman filter to estimate and fuse the UWB measurements, and the drift-free position obtained by the UWB localization Kalman filter was used to fuse the position calculated by IMU [9].…”

Section: Related Workmentioning

confidence: 99%

Improved Indoor Positioning Model Based on UWB/IMU Tight Combination with Double-Loop Cumulative Error Estimation

Zhu,

Zhao,

Zhang

et al. 2023

Applied Sciences

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With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).

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“…Due to its simplicity and effectiveness, as documented in multiple studies [30,31], the complementary filter was used to process the IMU signals in this application.…”

Section: Head-orientation-based Control Systemmentioning

confidence: 99%

Head and Voice-Controlled Human-Machine Interface System for Transhumeral Prosthesis

Molina Arias,

Iwaniec,

Pirowska

et al. 2023

Electronics

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The design of artificial limbs is a research topic that has, over time, attracted considerable interest from researchers in various fields of study, such as mechanics, electronics, robotics, and neuroscience. Continuous efforts are being made to build electromechanical systems functionally equivalent to the original limbs and to develop strategies to control them appropriately according to the intentions of the user. The development of Human–Machine Interfaces (HMIs) is a key point in the development of upper limb prostheses, since the actions carried out with the upper limbs lack fixed patterns, in contrast to the more predictable nature of lower limb movements. This paper presents the development of an HMI system for the control of a transhumeral prosthesis. The HMI is based on a hybrid control strategy that uses voice commands to trigger prosthesis movements and regulates the applied grip strength when the user turns his head. A prototype prosthesis was built using 3D technology and trials were conducted to test the proposed control strategy under laboratory conditions. Numerical simulations were also performed to estimate the grip strength generated. The results obtained show that the proposed prosthesis with the dedicated HMI is a promising low-cost alternative to the current solutions. The proposed hybrid control system is capable of recognizing the user’s voice with an accuracy of up to 90%, controlling the prosthesis joints and adjusting the grip strength according to the user’s wishes.

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IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation

Cited by 10 publications

References 38 publications

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Easy Rocap: A Low-Cost and Easy-to-Use Motion Capture System for Drones

Improved Indoor Positioning Model Based on UWB/IMU Tight Combination with Double-Loop Cumulative Error Estimation

Head and Voice-Controlled Human-Machine Interface System for Transhumeral Prosthesis

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