Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles within large-scale 3D scene models. However, generating high-precision maps is complex and costly, posing challenges to commercialization. As a result, a global localization system that employs low-precision, city-scale 3D scene maps reconstructed by unmanned aerial vehicles (UAVs) is proposed to optimize visual positioning for vehicles. To address the discrepancies in image information caused by differing aerial and ground perspectives, this paper introduces a wall complementarity algorithm based on the geometric structure of buildings to refine the city-scale 3D scene. A 3D-to-3D feature registration algorithm is developed to determine vehicle location by integrating the optimized city-scale 3D scene with the local scene generated by an onboard stereo camera. Through simulation experiments conducted in a computer graphics (CG) simulator, the results indicate that utilizing a completed low-precision scene model enables achieving a vehicle localization accuracy with an average error of 3.91 m, which is close to the 3.27 m error obtained using the high-precision map. This validates the effectiveness of the proposed algorithm. The system demonstrates the feasibility of utilizing low-precision city-scale 3D scene maps generated by unmanned aerial vehicles (UAVs) for vehicle localization in large-scale scenes.

In orthopedic surgeries, such as osteotomy and osteosynthesis, an intraoperative 3D reconstruction of the bone would enable surgeons to quickly assess the fracture reduction procedure with preoperative planning. Scanners equipped with such functionality are often more expensive than a conventional C-arm fluoroscopy device. Moreover, a C-arm fluoroscopy device is commonly available in many orthopedic facilities. Based on the widespread use of such equipment, this paper proposes a method to reconstruct the 3D structure of bone with a conventional C-arm fluoroscopy device. We focus on wrist bones as the target of reconstruction in this research as this will facilitate a flexible imaging scheme. Planar markers are attached to the target object and are tracked in the fluoroscopic image for C-arm pose estimation. The initial calibration of the device is conducted using a checkerboard pattern. In general, reconstruction algorithms are sensitive to geometric calibration errors. To assess the practicality of the method for reconstruction, a simulation study demonstrating the effect of checkerboard thickness and spherical marker size on reconstruction quality was conducted.

In recent years, research efforts have enabled using the internet in disaster areas, and information technology (IT) is expected to improve our comprehension and evaluation of disasters. In disaster areas, crowdsourcing can be employed to secure human resources and controlled-task distribution. In crowdsourcing, many workers with publicly defined tasks (also called microtasks) are used. A simplified and more efficient microtask framework is required for disaster areas due to the lack of human resources and facilities. In this paper, we describe a method that incorporates information collection from a disaster area into microtasks with machine processing support.

To compare a 3D preoperative planning image and fluoroscopic image, a 3D bone position estimation system that displays 3D images in response to changes in the position of fluoroscopic images was developed. The objective of the present study was to evaluate the accuracy of the estimated position of 3D bone images with reference to fluoroscopic images. Bone positions were estimated from reference points on a fluoroscopic image compared with those on a 3D image. The four reference markers positional relationships on the fluoroscopic image were compared with those on the 3D image to evaluate whether a 3D image may be drawn by tracking positional changes in the radius model. Intra-class correlations coefficients for reference marker distances between the fluoroscopic image and 3D image were 0.98–0.99. Average differences between measured values on the fluoroscopic image and 3D bone image for each marker corresponding to the direction of the bone model were 1.1 ± 0.7 mm, 2.4 ± 1.8 mm, 1.4 ± 0.8 mm, and 2.0 ± 1.6 mm in the anterior-posterior view, ulnar side lateral view, posterior-anterior view, and radial side lateral view, respectively. Marker positions were more accurate in the anterior-posterior and posterior-anterior views than in the radial and ulnar side lateral views. This system helps in real-time comparison of dynamic changes in preoperative 3D and intraoperative fluoroscopy images.

Objective: The purpose of this study was to quantitatively measure the split-step skills of the world’s top badminton players to clarify the characteristics underlying these skills when moving into the forehand position in the rear court. Methods: We analyzed the four best ranking players (1^st to 4^th) in the men’s singles competition at the World Badminton Federation (BWF) World Championships 2023, a world tournament whose match videos are available online. Analysis 1 was conducted to determine the location of the players’ feet on the court when performing a split-step while moving to the forehand rear court, as well as the width of the stance and the reaction time from that stance to taking the first step. To define the characteristics of top athletes, the split-step skill performance of these athletes was evaluated during play. Analysis 2 was used to determine whether the performance of the split-step when moving to the forehand rear court varied depending on the position of the opposing player. Results: Analysis 1 showed that the split-step position was gathered close to the base, with an average split-step reaction time of 0.25 s and a split-step stance width comprising 50% of the players’ height. These results were similar among all top players evaluated. Analysis 2 showed that the difference in the number of shuttlecocks that hit the opponent’s backhand rear court (LR) affected their degree of split-step skill. Conclusion: In this study, we quantitatively measured the split-step skills of the world’s top badminton athletes and clarified the characteristics of their positioning into the forehand rear court during active play. Herein, movement and performance analysis using match videos available online was used to gain novel insights into the performance of these athletes.