Tingting Chang

Ministry of Industry and Information Technology

Papers

Total Citations

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About

Tingting Chang is a leading researcher in autonomous navigation and robotics, specializing in LiDAR-based localization, simultaneous localization and mapping (SLAM), and integrated navigation systems. Her work addresses critical challenges in enabling robust robot autonomy, particularly in indoor and perceptually degraded environments where traditional methods fail. Chang’s most-cited paper, “An Indoor 2-D LiDAR SLAM and Localization Method Based on Artificial Landmark Assistance” (2023, 25 citations), introduces a landmark-assisted approach to overcome feature scarcity in indoor settings, significantly improving localization reliability. She further advanced the field with “LTI-SAM: Lidar-Template Matching-Inertial Odometry via Smoothing and Mapping” (2022, 12 citations), a framework that fuses LiDAR, template matching, and inertial data to maintain accurate odometry in challenging scenes like corridors, tunnels, and open mines. Her recent work, “Variational Bayesian-Based Adaptive Error-State Kalman Filter” (2024, 10 citations), tackles the real-world problem of unknown, time-varying noise statistics in integrated navigation systems, enhancing filter robustness for dynamic environments. With a growing citation impact, Chang’s innovations are pivotal for practical deployment of autonomous land vehicles, from indoor service robots to industrial and mining applications.