Titus Jia
Papers
1
Total Citations
8
H-Index
1
About
Titus Jia is a researcher whose work centers on robotics, computer vision, and real-time 3D perception, with a particular focus on egomotion estimation and simultaneous localization and mapping (SLAM). His most cited contribution, "A lightweight approach to 6-DOF plane-based egomotion estimation using inverse depth" (2011), presents a novel, real-time method for estimating the six-degree-of-freedom pose of a moving Microsoft Kinect. By leveraging planar surfaces and an inverse depth parametrization, Jia’s approach enables efficient self-localization even when accurate maps are unavailable—a critical capability for autonomous robots navigating unknown environments. This work has garnered 8 citations, reflecting its practical value in advancing lightweight, real-time SLAM solutions. Jia’s research bridges the gap between theoretical geometry and applied robotics, offering computationally efficient techniques that are accessible for real-world deployment. His contributions are particularly notable for addressing the challenges of low-cost sensor platforms, making robust egomotion estimation more attainable for a broader range of robotic systems. For students and researchers exploring visual odometry or 3D mapping, Jia’s work provides a foundational example of how plane-based methods and inverse depth can simplify complex estimation problems without sacrificing accuracy.
Research Focus
Key Achievements
Top Papers
- 1