Cailing Wang

Nanjing University of Science and Technology, Nanjing University of Posts and Telecommunications

Papers

Total Citations

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About

Cailing Wang is a researcher specializing in robotics perception, autonomous navigation, and sensor fusion, with a particular focus on vision-based ego-motion estimation and radar signal processing for intelligent vehicles. Her work addresses critical challenges in GPS-denied environments, where accurate positioning remains a fundamental hurdle for field robotics and autonomous driving. Wang’s most cited paper, “Monocular odometry in country roads based on phase-derived optical flow and 4-DOF ego-motion model” (2011, 11 citations), introduced a novel visual odometry strategy using a single camera, enabling robust localization in high-slip, unstructured terrains. She further advanced monocular motion estimation with a maximum likelihood optimization scheme for optical flow fields (2016, 4 citations), improving pose accuracy for mobile robots. In the domain of automotive radar, Wang developed a local resampling Fourier transform method for FMCW radar range estimation (2016, 5 citations), enhancing target discrimination in complex traffic scenarios. Her work on a robust vision system for space teleoperation ground verification (2013) demonstrates the breadth of her expertise, spanning cooperative object detection and pose estimation. With a career focused on practical, real-world sensing solutions, Wang’s contributions continue to support the development of reliable perception systems for autonomous platforms.