Papers
1
Total Citations
3
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About
Egor Ershov is a researcher specializing in computer vision and robotics, with a particular focus on visual localization and 3D perception for unmanned systems. His most cited work, "Stereo-based visual localization without triangulation for unmanned robotics platform" (2017), introduces a novel localization method that matches stereo images by minimizing the sum of squared distances between 3D points and their corresponding 3D rays. This approach bypasses traditional triangulation, offering enhanced robustness for practical robotics applications. With over 3 citations, this paper demonstrates early impact in the field. Ershov’s contributions address critical challenges in autonomous navigation, particularly for platforms operating in unstructured or GPS-denied environments. His work is notable for its practical emphasis on real-world deployment, bridging the gap between theoretical computer vision algorithms and reliable robotic localization. For students and researchers exploring visual odometry or SLAM, Ershov’s research offers a compelling example of how innovative geometric methods can improve system resilience and accuracy in field robotics.
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