Remote photoplethysmography (rPPG) for heart rate (HR) measurement based on facial videos has recently attracted increasing attention. However, most existing methods focus on average heart rate (AHR) over a period rather than instantaneous heart rate (IHR), which better reflects physical and mental states. To address this issue, we propose a novel rPPG-based method for measuring IHR values from facial videos. Our method employs the wavelet synchrosqueezed transform (WSST) to generate time-frequency representations (TFRs) of chrominance (CHROM) signals from multiple facial regions of interest (ROIs), synchronously reflecting the IHR during a video segment. Furthermore, the TransUNet is introduced to refine these TFR images, enhancing the ridge line information related to IHRs. Comprehensive comparisons and ablation studies on four public datasets (UBFC-rPPG, PURE, UBFC-Phys, and MMPD) reveal that our WSST-UNet method achieves superior performance over several typical rPPG methods, achieving mean absolute errors (MAE) of 2.34 beats per minute (bpm), 1.29 bpm, 5.03 bpm, and 6.58 bpm, respectively. The proposed method offers a promising solution for practical application in video-based IHR measurements.

It is challenging to employ a quadruped robot for real-time mapping and positioning in a large range of scenes. The significant vibration and instability of the quadruped robot during mobility, as well as the quantity of computation required to convey a wide variety of complex landscapes, result in unsatisfactory drawing construction accuracy and inefficient real-time performance. Therefore, we propose an accurate robust spinning LiDAR SLAM (ARS-SLAM) algorithm for a quadruped robot under the large-scale scene. The tightly coupled iterative Kalman filter in FAST-LIO2 is introduced into the front end of the cartographer framework to improve the accuracy and robustness of robot pose estimation. To reduce the computational complexity of the original cartographer framework, a pose threshold optimization algorithm was introduced to effectively remove redundant information from loop detection and improve computational efficiency and real-time performance. We tested the system's performance against the most advanced point-cloud-based methods, LIO-SAM and FAST-LIO2, on a large dataset of large science parks and underground parking lots, and the results show that the proposed system achieves the same or better accuracy and real-time performance.