The ability to estimate joint parameters is essential for various applications in robotics and computer vision. In this paper, we propose CAPT: category-level articulation estimation from a point cloud using Transformer. CAPT uses an end-to-end transformer-based architecture for joint parameter and state estimation of articulated objects from a single point cloud. The proposed CAPT methods accurately estimate joint parameters and states for various articulated objects with high precision and robustness. The paper also introduces a motion loss approach, which improves articulation estimation performance by emphasizing the dynamic features of articulated objects. Additionally, the paper presents a double voting strategy to provide the framework with coarse-to-fine parameter estimation. Experimental results on several category datasets demonstrate that our methods outperform existing alternatives for articulation estimation. Our research provides a promising solution for applying Transformer-based architectures in articulated object analysis.

We propose an extrinsic calibration method for LiDAR-camera fusion systems using variations in intensities projected from camera images to the LiDAR point cloud. As the input, the proposed method uses a sequence of LiDAR data and camera images captured while moving the system. Once the camera motion is calculated, camera images are projected onto the point cloud. The variations in the projected intensities at each point are large in the presence of errors in the estimated motion or calibration parameters. Consequently, the extrinsic parameters are optimized for cost minimization based on the intensity variance. In addition, a suitable geometry is proposed for the calibration and verified using simulations. Our experimental results showed that the proposed method accurately performed calibrations using a camera and a sparse multi-beam LiDAR or one-dimensional LiDAR.

There is an increasing demand for biometric security systems in several fields. This study presents a highly accurate facial recognition method that uses high-speed transformation and facial morphing using region-limited log-polar transformation based on a center point calculated from the coordinates of both eyes and corners of the mouth. Log-polar transformation is limited to the region, so that the region including the feature can be suppressed to the minimum range, thereby facilitating high-speed transformation. Additionally, after facial morphing, the shapes of the eyes and mouth are altered based on the outline of the face, enabling high-precision facial recognition. The efficacy of the proposed method is verified experimentally. Therefore, we can confirm that 91.72% of images using the color FERET database and 96% of images using the FEI face database can be recognized using our method.

3D Sensing systems mounted on mobile platform are emerging and have been developed for various applications. In this paper, we propose a profiler scanning system mounted on a rover to scan and reconstruct a bas-relief with high density and accuracy. Our hardware system consists of an omnidirectional camera and a 3D laser scanner. Our method selects good projection points for tracking to estimate motion stably and reject mismatches caused by difference between the positions of laser scanner and camera using an error metric based on the distance from omnidirectional camera to scanned point. We demonstrate that our results has better accuracy than comparable approach. In addition to local motion estimation method, we propose global poses refinement method using multi modal 2D-3D registration and our result shows good consistency between reflectance image and 2D RGB image.

In recent years, the research of humanoid robots that replace human tasks in emergency situations have been widely studied. Currently, many approaches are automate dedicated hardware for each mission. But, at the environment where situation changes, operation by humanoid robot is effective to operate equipments which designed for human. Ultimately, automation is ideal, but under the present circumstances, teleoperation of humanoid robot is effective for corresponding changes of situation. An intuitive interface is required for effectively controlling the humanoid robot from a distant place. Recently, the interfaces that map the human motion to the humanoid robot have become popular because of the development of the motion recognition systems. However, the humanoid robot and human beings have different joint structure, physical ability and weight balance. It is not practical to map the motion directly. There is also the issue of time delay between the operator and the robot. Therefore, it is desirable that the operator performs global judgments and the robot runs semi-autonomously in the local environment. In this paper we propose a method to remotely operate the humanoid robot by the task model. Our method describes human behavior abstractly by the task model and mapped this abstract expressions to humanoid robots, and overcome difference of structure of body. In this work, we operate lever of buggy-type vehicles as a example of mapping using the task model.

The goal of this work is to teach a robot to regrasp an object using knowledge obtained from human demonstration. This paper presents a task model that represents a human regrasping movement. The task model is based on the topological information and comprised of four task primitives. Human regrasping movement is recognised and represented as a sequence of these task primitives by the proposed recognition algorithm. The proposed method then maps each task primitive to the target robot hand using knowledge obtained from human demonstration. The experimental result verified the proposed task model by executing the regrasping movement on the real robot hand.

In this paper, GaN power transistors on Si substrates for power switching application are reported. GaN heterojunction field-effect transistor (HFET) structure on Si is an important configuration in order to realize a low loss and high power devices as well as one of the cost-effective solutions. Current collapse phenomena are discussed for GaN-HFETs on Si substrate, resulting in suppression of the current collapse due to using the conducting Si substrate. Furthermore, attempts for normally off GaN-FETs were examined. A hybrid metal-oxide-semiconductor HFET structure is a promising candidate for obtaining devices with a lower on-resistance (Ron) and a high breakdown voltage (Vb).

In order to transmit, share and store human knowledge, it is important for a robot to be able to acquire task models and skills by observing human actions. Since vision plays an important role for observation, we propose a technique for measuring the position and posture of objects and hands in 3-dimensional space at high speed and with high precision by vision. Next, we show a framework for the analysis and description of a task by using an object functions as elements.