Yong Lv
Papers
8
Total Citations
78
H-Index
4
About
Yong Lv is a researcher advancing autonomous robotics and computer vision, with a focus on efficient exploration and object detection for intelligent systems. His primary contributions lie in developing heuristic-driven exploration methods for mobile robots, notably through RRT-based algorithms that improve frontier extraction and path planning in unknown environments. His 2022 paper "An Efficient Robot Exploration Method Based on Heuristics Biased Sampling" (27 citations) and related work on prior information heuristics (11 citations) demonstrate his impact on autonomous navigation. Lv also addresses practical challenges in service robotics, creating the ODSR-IHS benchmark dataset (20 citations) for object detection in home scenes and designing lightweight networks like inverted residual-based detectors and YOLOv5 adaptations for sweeping robots. His work spans from UWB-based indoor navigation to advanced frameworks like GVD-Exploration (2024), which integrates Voronoi diagrams for faster exploration. With over 75 cumulative citations across his most-cited papers, Lv’s research bridges theoretical efficiency gains with real-world applications, making his work valuable for students and engineers developing autonomous robots for domestic and industrial settings.
Research Focus
Key Achievements
Top Papers
- 1An Efficient Robot Exploration Method Based on Heuristics Biased Sampling27 citations · 2022
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- 8YOLO_SRv2: An evolved version of YOLO_SR2 citations · 2023