Xueyu Feng

Reliable semantic perception is crucial for service robots operating in complex public indoor environments. However, existing semantic mapping approaches often face the dual challenges of high computational overhead and semantic redundancy in maps. To address these limitations, this paper proposes a low-resource semantic mapping framework based on improved instance segmentation and dynamic constraints from consecutive frames. First, we design the lightweight model MS-YOLO, which adopts MobileNetV4 as its backbone network and incorporates the SHViT neck module, effectively optimizing the balance between detection accuracy and computational cost. Second, we propose a consecutive frame dynamic constraint method that eliminates redundant object annotations through consecutive frame stability verification. Experimental results relating to both fusion and custom datasets demonstrate that compared to YOLOv8n-seg, MS-YOLO achieves improvements in accuracy, recall, and mAP@0.5, while reducing the number of parameters by 11.7% and floating-point operations (FLOPs) by 32.2%. Furthermore, compared to YOLOv11n-seg and YOLOv5n-seg, its FLOPs are reduced by 17.2% and 25.5%, respectively. Finally, the successful deployment and field validation of this system on the Jetson Orin NX platform demonstrate its real-time capability and engineering practicality for edge computing in public indoor service robots.

Achieving accurate and high-sensitivity liquid level detection in medical instruments has always been a knotty task. In this paper, a high-precision, non-contact, flexible capacitive liquid level sensor is proposed, aiming to apply capacitive sensors in test tube liquid level measurement and improving the sensitivity of real-time liquid level sensors. The simulation study is conducted using ANSYS Maxwell and demonstrates the correlation between test tube thickness and sensitivity. A geometric model of the test container and sensing electrodes is established to optimize the design strategy for the physical dimensions of the sensor’s interdigitated (IDT) electrodes based on a flexible printed circuit (FPC). The hardware and software designs are completed based on the FDC2214 capacitive-to-digital converter to collect the capacitance variation data of the sensing electrodes accurately. To assess the system’s performance, an experimental platform for a liquid level sensor system has been constructed, facilitating the measurement, communication, processing, and visualization of liquid levels. The performance results demonstrate that the system is capable of accurately measuring the effective liquid level range within a standard 5 mL test tube with a resolution of up to 1 mm, as well as a sensitivity of 78.68 fF/mm, verifying the simulation results and exhibiting excellent linearity.