Dennis Lui

Papers

Total Citations

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About

Dennis Lui is a robotics researcher whose work focuses on real-time egomotion estimation and visual perception for mobile robots, particularly in challenging outdoor environments. His most significant contribution is a lightweight, plane-based approach to 6-DOF egomotion estimation using inverse depth, published in 2011. This method enables a moving Microsoft Kinect to self-localize in real time without relying on a pre-existing map, a critical capability for simultaneous localization and mapping (SLAM) systems. The work, which has garnered 8 citations, addresses a fundamental bottleneck in autonomous navigation by providing a computationally efficient solution for pose tracking. Earlier in his career, Lui explored omnidirectional vision systems for outdoor mobile robots (2008), leveraging advances in microprocessor and image sensor technology to capture rich environmental data. While his citation counts remain modest, his contributions are notable for their practical, real-time applicability—a key requirement for field robotics. Lui’s research bridges the gap between theoretical SLAM algorithms and deployable systems, making him a relevant figure for students and engineers interested in low-latency visual odometry and sensor integration for autonomous platforms.