Keng Kiat Lim
Papers
3
Total Citations
75
H-Index
3
About
Keng Kiat Lim is a robotics researcher whose work centers on persistent mobile robot localization and environmental mapping, with a particular focus on leveraging Gaussian processes (GPs) to handle spatially correlated measurements. His major contribution is the development of the GP-Localize algorithm, a novel approach that enables robots to maintain accurate localization over long durations by using an online sparse Gaussian process observation model. This method stands out for its ability to exploit spatial correlations in sensor data, making it highly effective for persistent operation in dynamic or feature-sparse environments. The core paper on GP-Localize has garnered 44 citations, reflecting its influence in the field of robot exploration and mapping. Lim further extended this work with a generalized online sparse GP framework, detailed in a related paper with 15 citations, which broadens the applicability of his approach. His research addresses a fundamental challenge in robotics—how to keep a robot reliably localized as it explores—and offers practical, computationally efficient solutions that have been well received by the community.
Research Focus
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Top Papers
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