Erik Beerepoot

Papers

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About

Erik Beerepoot is a robotics researcher whose work centers on visual navigation and perception for mobile robots operating in challenging, unstructured environments. His most influential contribution, the paper "Into Darkness: Visual Navigation Based on a Lidar-Intensity-Image Pipeline" (2016, with 34 citations), addresses a critical gap in autonomous navigation: the failure of passive camera systems in low-light or darkness. Beerepoot pioneered a novel approach that repurposes lidar intensity data—traditionally used only for ranging—to generate synthetic images, enabling robust visual odometry and path planning even in complete darkness. This work bridges the gap between lidar and vision-based methods, offering a practical solution for applications ranging from planetary exploration to subterranean search-and-rescue. By demonstrating that lidar can serve as a "camera" in extreme lighting conditions, Beerepoot has advanced the reliability of autonomous systems in real-world, round-the-clock operations. His research continues to influence the design of perception pipelines for field robotics, where robustness to environmental extremes is paramount.