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About

Chang Chen is a leading researcher in the field of robotics, specializing in visual-inertial simultaneous localization and mapping (VI-SLAM) for autonomous navigation. Their seminal review, "A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives" (2018), with 122 citations, provides a comprehensive taxonomy of VI-SLAM approaches, establishing a foundational reference for the community. Chen’s major contributions include developing robust SLAM systems for challenging environments, such as indoor spaces with low or repeating textures, as demonstrated in their 2019 work on stereo visual-inertial SLAM for unknown environments (27 citations). They have also advanced VI-SLAM for GPS-denied settings, using optical flow methods to ensure accurate positioning in obstructed areas like buildings and forests (13 citations). Notably, their keyframe-based stereo visual-inertial SLAM using nonlinear optimization (2018) achieves high accuracy by tightly coupling camera and IMU data through pre-integration, pushing the boundaries of precision for autonomous robots. Chen’s work is instrumental in enabling reliable navigation for mobile robots, self-driving cars, and unmanned aerial vehicles, making them a pivotal figure in modern robotics research.