Christian Gebken

About

Christian Gebken is a researcher whose work lies at the intersection of computer vision and robotics, with a focused expertise in omnidirectional imaging and pose estimation. His major contributions center on developing robust methods for determining the position and orientation of a camera—or robot—from visual data captured by wide-angle, catadioptric sensors. In his seminal papers, including "Perspective Pose Estimation from Uncertain Omnidirectional Image Data" and "Pose Estimation from Uncertain Omnidirectional Image Data Using Line-Plane Correspondences," both published in 2006, Gebken introduced novel approaches that leverage small data sets and line-plane correspondences to solve perspective pose problems. Critically, he integrated stochastic methods to explicitly handle measurement uncertainties, a practical advancement for real-world robot navigation. While his citation counts (2 each) reflect a niche but foundational impact, his work is notable for its rigorous treatment of uncertainty in omnidirectional vision—a key challenge for autonomous systems operating in complex environments. Gebken’s contributions provide essential building blocks for researchers in visual SLAM and sensor fusion, demonstrating how precise geometric estimation from imperfect data can enable more reliable robotic perception.

Research Focus

Key Achievements

Top Papers

1. 1
   Perspective Pose Estimation from Uncertain Omnidirectional Image Data

   2 citations · 2006
2. 2
   Pose Estimation from Uncertain Omnidirectional Image Data Using Line-Plane Correspondences

   2 citations · 2006

Key Collaborators

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