The current solutions for pose estimation problems using coplanar feature points (PnP problems) can be divided into non-iterative and iterative solutions. The accuracy, stability, and efficiency of iterative methods are unsatisfactory. Therefore, non-iterative methods have become more popular. However, the non-iterative methods only consider the correspondence of the feature points with their 2D projections. They ignore the constraints formed between feature points. This results in lower pose estimation accuracy and stability. In this work, we proposed an accurate and stable pose estimation method considering the line constraints between every two feature points. Our method has two steps. In the first step, we solved the pose non-iteratively, considering the correspondence of the 3D feature points with their 2D projections and the line constraints formed by every two feature points. In the second step, the pose was refined by minimizing the re-projection errors with one iteration, further improving accuracy and stability. Simulation and actual experiment results show that our method’s accuracy, stability, and computational efficiency are better than the other existing pose estimation methods. In the -45° to +45° measuring range, the maximum angle measurement error is no more than 0.039°, and the average angle measurement error is no more than 0.016°. In the 0 mm to 30 mm measuring range, the maximum displacement measurement error is no more than 0.049 mm, and the average displacement measurement error is no more than 0.012 mm. Compared to other current pose estimation methods, our method is the most efficient based on guaranteeing measurement accuracy and stability. Keywords:

This paper presents an estimation method for the spatial pose and displacement parameters of multi-rod suspension mechanism, based on measurement results by using wire sensors. Some changes of position and orientation of the platform fixed to wheel knuckle cause corresponding changes of sensors’ cable lengths. The fixation points of the cable sensors are selected with the collision-free conditions taken into account. Numerical example deals with platform poses and positioning of the sensors that satisfy the measurement conditions.

Background: The aim of the study was to answer two questions: 1 – Can data processing algorithms ensure sufficient accuracy for estimating human body pose via wearable systems? 2 – How to process the IMU sensor data to obtain the most accurate information on the human body pose? To answer these questions, the authors evaluated proposed algorithms in terms of accuracy and reliability. Methodology: data acquisition was performed with tested IMU sensors system mounted onto a Biodex System device. Research included pendulum movement with seven angular velocities (10-120°/s) in five angular movement ranges (30-120°). Algorithms used data from accelerometers and gyroscopes and considered complementary and/or Kalman filters with adjusted parameters. Moreover, angular velocity registration quality was also taken into consideration. Results: differences between means for angular velocity were 0.55÷1.05°/s and 1.76÷3.11%. In the case of angular position relative error of means was 4.77÷10.84%, relative error of extreme values was 2.15÷4.81% and Spearman’s correlation coefficient was 0.74÷0.89. Conclusions: Algorithm calculating angles based on acceleration-derived quaternions and with implementation of Kalman filter was the most accurate for data processing and can be adapted for future work with IMU sensors systems, especially in wearable devices that are designated to support human in daily activity.

The use of technology in sports has increased in recent years. One of the most influential of these technologies is referee support systems. Team sports such as volleyball require accurate and robust tracking systems that do not affect either the players or the court. This paper introduces the application of intrinsic and extrinsic camera calibration in a 12-camera volleyball referee system. Intrinsic parameters are calculated by using the classic pinhole model and Zhang’s method. To perform extrinsic calibration in real time, the volleyball court is treated as a global calibration artifact. Calibration keypoints are defined as court-line intersections. In addition, a new keypoint detection algorithm is proposed. It enables achievement of an accurate camera pose in regard to the court. With all 12 cameras calibrated in a common coordinate system, a dynamic camera stereo pair creation is possible. Therefore, with known ball 2D image coordinates, the 3D real ball coordinates can be reconstructed and the ball trajectory can be estimated. The performance of the proposed method is tested on a synthetic data set, including 3Ds Max rendering and real data scenarios. The mean camera pose error calculated for data biased with keypoint detection errors is approximately equal to 0.013% of the measurement volume. For the real data experiment with a human hand phantom, it is possible to determine the presence of the human phantom on the basis of the ball reflection attitude.