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About

Qiang Lv is a robotics researcher whose work spans SLAM-based perception, multi-robot systems, and intelligent navigation. His most cited paper, "ORB-SLAM-based tracing and 3D reconstruction for robot using Kinect 2.0" (29 citations), demonstrates his early focus on real-time dense 3D reconstruction by extending ORB-SLAM with RGB-D sensing for robot localization and environmental mapping. Lv has also made notable contributions to multi-robot coordination, designing a centralized distributed research platform using ultra-wideband (UWB) technology within the ROS framework (10 citations). His work on mapless navigation is particularly impactful: he pioneered deep reinforcement learning approaches with continuous action spaces (8 citations) and developed a model-free Q-learning method using lidar input for collision-free navigation (7 citations). Earlier in his career, Lv contributed to control theory with research on LQR controllers for two-wheeled self-balancing robots (5 citations), and more recently tackled fast, smooth trajectory planning for linear systems by reducing computational complexity through parameter and constraint reduction (2022). Lv’s research trajectory shows a clear evolution from foundational control and SLAM to modern learning-based navigation, making his work relevant for students and researchers interested in autonomous robotics, sensor fusion, and intelligent motion planning.