Xiaochuang Huo

Papers

Total Citations

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About

Xiaochuang Huo is a researcher advancing the frontiers of autonomous vehicle perception and localization, with a primary focus on 3D LiDAR SLAM (Simultaneous Localization and Mapping) and visual SLAM systems. Huo’s most impactful work tackles a critical challenge in autonomous navigation: reducing cumulative error in 3D LiDAR SLAM. In their 2021 paper, which has garnered 9 citations, Huo proposed a novel method that integrates ground segmentation with Scan Context loop detection, significantly improving the accuracy and robustness of vehicle SLAM in complex environments. This contribution addresses a hot issue in the field, offering a practical solution for long-term autonomous operation. Additionally, Huo has explored visual SLAM enhancement, applying an improved fast corner detection algorithm to ORB-SLAM2 in a 2022 study. While still early in their career, Huo’s work demonstrates a clear trajectory toward solving fundamental problems in spatial perception for robotics and autonomous systems. Their research is particularly relevant for students and engineers seeking to understand how sensor fusion and feature extraction can elevate SLAM performance in real-world, dynamic settings.