Chao Guo

University of Minnesota, Google (United States)

Papers

Total Citations

189

H-Index

About

Chao Guo is a leading researcher in robotics and autonomous systems, with a primary focus on visual-inertial navigation, sensor calibration, and state estimation for wheeled and mobile robots. His most impactful contribution is the seminal work "VINS on wheels" (2017, 170 citations), which advanced the theoretical understanding of vision-aided inertial navigation systems (VINS) by proving that ground vehicles constrained to straight-line or circular-arc motion introduce additional unobservable directions, such as scale. This insight is critical for improving localization accuracy in real-world robotic applications. Guo also made notable strides in sensor fusion, as demonstrated in his 2012 paper on IMU-RGBD camera extrinsic calibration (5 citations), where he rigorously analyzed the observability of the nonlinear calibration system and proved that calibration parameters are fully observable under specific conditions. More recently, his 2022 work on learned monocular depth priors for visual-inertial initialization (14 citations) bridges deep learning with classical estimation, enhancing robustness in challenging environments. Guo’s research is highly influential for students and engineers working on autonomous navigation, offering both theoretical foundations and practical algorithms for reliable robot localization.