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About

Kaiqiao Tian is a robotics and autonomous systems researcher whose work centers on multi-sensor fusion, state estimation, and mobile robot navigation. His most impactful contribution, "Comparing EKF, UKF, and PF Performance for Autonomous Vehicle Multi-Sensor Fusion and Tracking in Highway Scenario" (17 citations), provides a critical benchmark for Kalman filter and particle filter algorithms in real-world tracking problems, directly addressing the safety and perception needs of self-driving vehicles. Tian also developed sensor fusion architectures for the Octagon robot (4 citations), enabling seamless indoor-outdoor navigation using LiDAR, RADAR, and depth cameras. His research extends to quadruped robot locomotion, where he designed a stable trot gait control system using IMU sensors (4 citations), and to software engineering for robotics, proposing a Docker-based rapid prototyping framework for ROS-to-ROS2 migration (2 citations). Most recently, Tian introduced a Singular Value Decomposition (SVD) method for LiDAR-camera fusion and pattern matching (2025), offering a novel solution to the persistent challenge of aligning disparate sensor modalities. His work consistently bridges theoretical estimation algorithms with practical robotic deployment, making him a notable contributor to the fields of autonomous navigation and sensor integration.