Chengyuan Lin

Papers

Total Citations

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About

Chengyuan Lin is a robotics researcher whose work focuses on advancing long-term autonomy through dense mapping and change detection. His key contributions lie in developing methods that enable robots to maintain consistent environmental understanding across multiple operational sessions—a critical capability for systems deployed over extended periods. Lin’s most cited paper, “PlaneSDF-Based Change Detection for Long-Term Dense Mapping” (2022), introduces a novel approach that leverages plane-augmented signed distance functions to detect discrepancies between maps from different sessions. This work directly addresses the challenge of conflict-free environmental perception, allowing autonomous agents to distinguish between transient and permanent changes in dynamic settings. With 5 citations, this research has already garnered attention for its practical implications in persistent robotic operations. Lin’s achievements demonstrate a clear commitment to solving fundamental problems in simultaneous localization and mapping (SLAM), particularly the integration of geometric primitives for robust multi-session mapping. His work is especially relevant for applications in long-term surveillance, autonomous inspection, and any domain requiring reliable robot operation over days or weeks without human intervention.