Xingliang Dong

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About

Xingliang Dong is a robotics researcher whose work lies at the intersection of sensor fusion, simultaneous localization and mapping (SLAM), and cooperative perception. His most impactful contribution, “Cooperative localization for disconnected sensor networks and a mobile robot in friendly environments” (2017), has earned 25 citations and addresses a critical challenge in multi-agent systems: enabling a mobile robot to localize itself and disconnected sensor nodes without continuous communication. Dong’s approach leverages intermittent encounters to build a shared spatial understanding, advancing the practicality of distributed robotic teams. In visual place recognition, a notoriously difficult problem in SLAM due to drastic changes in lighting, weather, and viewpoint, Dong proposed a novel image-sequence-based method (2019, 14 citations). Rather than relying on single-frame descriptors, his technique exploits temporal continuity across image sequences to achieve more robust place recognition—a key enabler for long-term autonomous navigation. He further explored hybrid sensing by fusing Wi-Fi signal strength with RGB-D images for SLAM (2018, 5 citations), demonstrating how ubiquitous wireless infrastructure can support large-scale indoor mapping without expensive sensors. Dong’s research consistently pushes toward more resilient, real-world robotic systems that can operate under uncertainty and limited connectivity.