In this article, a novel sensing structure is proposed to measure tensile loading. The sensing structure is motivated by the operating principle of the tendon. The fibroelastic characteristic of the tendon is implemented by combining sewing and elastic threads with a piezoelectric material, that is, a tendon-inspired piezoelectric sensor. The mechanical deformation of the tendon-inspired piezoelectric sensor was analyzed by applying tensile loading. Additionally, the electrical response according to the given tensile loading was measured and compared with the theoretical expectation. The experimental results found that the sensor outputs can be represented as a linear function along with elongation in a specific range. Furthermore, the motion-detecting capability of the sensor was demonstrated using body supports with seven tendon-inspired piezoelectric sensors. To show the feasibility of its application, a real-time motion detecting system using a virtual avatar was developed. The avatar showed good synchronization with the subject's upper-limb movements. This study provides insight into the sensing characteristics of the tendon-driven piezoelectric sensor and shows the potential for biometric applications.

This paper presents a novel structure-from-motion methodology using 2D lidars (Light Detection And Ranging). In 3D space, 2D lidars do not provide sufficient information for pose estimation. For this reason, additional sensors have been used along with the lidar measurement. In this paper, we use a sensor system that consists of only 2D lidars, without any additional sensors. We propose a new method of estimating both the 6D pose of the system and the surrounding 3D structures. We compute the pose of the system using line segments of scan data and their corresponding planes. After discarding the outliers, both the pose and the 3D structures are refined via nonlinear optimization. Experiments with both synthetic and real data show the accuracy and robustness of the proposed method.

We present a novel simultaneous localization and mapping (SLAM) system that extends the state-of-the-art ORB-SLAM2 for multi-camera usage without precalibration. In this system, each camera is tracked independently on a shared map, and the extrinsic parameters of each camera in the fixed multi-camera system are estimated online up to a scalar ambiguity (for RGB cameras). Thus, the laborious precalibration of extrinsic parameters between cameras becomes needless. By optimizing the map, the keyframe poses, and the relative poses of the multi-camera system simultaneously, observations from the multiple cameras are utilized robustly, and the accuracy of the shared map is improved. The system is not only compatible with RGB sensors but also works on RGB-D cameras. For RGB cameras, the performance of the system tested on the well-known EuRoC/ASL and KITTI datasets that are in the stereo configuration for indoor and outdoor environments, respectively, as well as our dataset that consists of three cameras with small overlapping regions. For the RGB-D tests, we created a dataset that consists of two cameras for an indoor environment. The experimental results showed that the proposed method successfully provides an accurate multi-camera SLAM system without precalibration of the multi-cameras.

A multisensor fusion‐based unknown object detection and tracking approach is presented. The approach consists of two new algorithms: (i) object segmentation by boundary detection and (ii) data association by integer programming. The proposed approach tightly combines the information from a single‐line scanning lidar and a camera. The performance of the proposed algorithm is evaluated by comparing with a state‐of‐the‐art method and is demonstrated with real datasets obtained from a moving platform.