Daniel N. Conrad
Papers
1
Total Citations
45
H-Index
1
About
Daniel N. Conrad is a robotics researcher whose work centers on computer vision, autonomous navigation, and perceptual systems for mobile robots. His most influential contribution, the 2010 paper "Homography-based ground plane detection for mobile robot navigation using a Modified EM algorithm," has garnered 45 citations and introduced a novel approach to a fundamental challenge in robotics: reliably distinguishing traversable ground from obstacles. Conrad’s key innovation was applying a Modified Expectation Maximization algorithm to cluster image pixels into ground and non-ground classes using homography constraints from stereo image pairs. This method provided a robust, real-time solution for ground plane detection, directly enabling safer and more efficient autonomous navigation in unstructured environments. By addressing the critical problem of perceptual grounding with statistical rigor, Conrad’s work has informed subsequent research in field robotics, visual odometry, and off-road navigation. His contributions demonstrate a practical engineering mindset, bridging theoretical computer vision with real-world robotic deployment, and remain a touchstone for researchers developing perception systems for mobile platforms operating in complex, dynamic terrains.
Research Focus
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Top Papers
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