In this letter, we propose a novel Non-Line-of-Sight (NLOS) identification and error-mitigation method for dynamic object positioning and ultra-wideband (UWB) ranging. By utilizing inverse estimation on known Anchor Points (APs) and improved robust unscented Kalman filter (IRUKF), while fusing Gyroscope and Accelerometer data, the proposed technology identifies and compensates for NLOS occlusions between tag and APs, reducing positioning errors. The approach has been verified through simulation and experiment, with identification precision of 97.02%. After mitigating errors, substantial error reductions of 91.80% and 98.90% were observed in LOS and NLOS situations, respectively. Moreover, the developed IRUKF effectively minimizes mislocalization by 50.48% in harsh scenarios.

The mitigation of the drift of the odometer and the ghost trail effect in the map is a challenge for simultaneous localization and mapping (SLAM). Facing the challenge, a novel method, called LIWOM-GD, is proposed via factor graph optimization, which tightly fuses LiDAR, IMU, and wheel encoder measurements. Firstly, the front-end module provides undistorted LiDAR scans, the pose states to be optimized, and the LiDAR-Inertial (LI) odometry factor. Secondly, the point cloud and pre-integration processing module implements ground segmentation, IMU, and encoder pre-integration to form the ground plane, IMU, and encoder factors. Furthermore, an enhanced dynamic point removal method is integrated into the processing module. Thirdly, a typical loop closure detection method is used to obtain a loop closure factor. Finally, all the above factors are added into the factor graph to obtain optimized results based on the GTSAM library. The proposed SLAM system tightly fuses three sensor measurements based on the factor graph, adding global plane constraint and dynamic points removal module based on spatiotemporal normals. Experimental tests are conducted on both benchmark and self-collected datasets. The results demonstrate that our method not only achieves more robust localization and mapping in degraded environments but also effectively implements dynamic point removal compared with the state-of-the-art SLAM methods.

Ultrafast laser welding of transparent materials has been widely used in sensors, microfluidics, optics, etc. However, the existing ultrafast laser welding depths are limited by the short laser Rayleigh length, which makes it difficult to realize the joining of transparent materials in the millimeter depth range and becomes a new challenge. Based on temporal shaping, we realized Burst mode ultrafast laser output with different sub-pulse numbers and explored the effect of different Burst modes on the welding performance using high-speed shadow in situ imaging. The experimental results show that the Burst mode femtosecond laser (twelve sub-pulses with a total energy of 28.9 μJ) of 238 fs, 1035 nm and 1000 kHz can form a molten structure with a maximum depth of 5 mm inside the quartz, and the welding strength can be higher than 18.18 MPa. In this context, we analyzed the transient process of forming teardrop molten structures inside transparent materials using high-speed shadow in situ imaging detection and systematically analyzed the fracture behavior of the samples. In addition, we further reveal the Burst femtosecond laser welding mechanism of transparent materials comprehensively by exploring the difference in welding performance under the effect of Burst modes with different sub-pulse numbers. This paper is the first to realize molten structures in the range of up to 5 mm, which is expected to provide a new welding method for curved surfaces and large-size transparent materials, helping to improve the packaging strength of photoelectric devices and the window strength of aerospace materials.

The essence of the difficulties for weld surface detection is that there is a lot of interference information during detection. This study aims to enhance the detection accuracy while keeping great deployment capabilities of a detection model for weld surface defects. To achieve this goal, an improved Yolo-graph convolution head (GCH) model is proposed based on the stable and fast Yolo-v5. The improvements primarily involve introducing a graph convolution network combined with a self-attention mechanism in the head part (i.e. GCH). This component focuses on improving the insufficient recognition capability of convolutional neural networks for similar defects in complex environments. Furthermore, to address the presence of potentially ambiguous samples in complex welding environments, the label assignment strategy of simOTA is implemented to optimize the anchor frame. Additionally, a streamlined structure, aiming to improve model detection speed while minimizing performance impact, has been designed to enhance the applicability of the model. The results demonstrate that the cooperation of GCH and simOTA significantly improves the detection performance while maintaining the inference speed. These strategies lead to a 2.5% increase in mAP@0.5 and reduce the missing detection rates of weld and 8 types of defects by 32.9% and 84.1% respectively, surpassing other weld surface detection models. Furthermore, the impressive applicability of the model is verified across four scaled versions of Yolo-v5. Based on the proposed strategies, the FPS increases by more than 30 frames in the fast s and n versions of Yolo-v5. These results demonstrate the great potential of the model for industrial applications.