Erik Stenborg

Papers

Total Citations

150

H-Index

About

Erik Stenborg is a leading researcher in computer vision, specializing in long-term visual localization and semantic mapping for autonomous systems. His work addresses the critical challenge of enabling robots and autonomous vehicles to reliably determine their position in dynamic environments over extended periods, despite changes in lighting, weather, and seasonal appearance. His most influential contribution, the 2018 paper "Semantic Match Consistency for Long-Term Visual Localization" (150 citations), introduced a novel framework that leverages semantic information—such as the classification of objects like buildings, roads, and trees—to improve the robustness of visual place recognition. By enforcing consistency between semantic matches across different times of day or year, Stenborg's method significantly reduces false positives and enhances localization accuracy in real-world conditions. This work has become a foundational reference for researchers tackling long-term autonomy, demonstrating how high-level scene understanding can bridge the gap between short-term and persistent navigation. Stenborg's research continues to push the boundaries of reliable visual perception, making him a key figure in the advancement of robust, long-term localization for robotics and autonomous driving.