Juan Castorena

Papers

Total Citations

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About

Juan Castorena is a leading researcher in autonomous vehicle perception, specializing in LIDAR-based localization, sensor calibration, and environmental mapping. His most influential work, "Ground-Edge-Based LIDAR Localization Without a Reflectivity Calibration for Autonomous Driving" (2017, 36 citations), introduces a novel edge reflectivity grid representation that enables robust localization across multiple LIDAR systems without requiring reflectivity calibration—a critical advancement for real-world autonomous driving. Castorena further advanced the field with "Motion Guided LiDAR-Camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles" (2020, 7 citations), which addresses the challenging problem of automatic sensor fusion between LIDAR and cameras. His foundational research on "Computational Mapping of the Ground Reflectivity With Laser Scanners" (2019, 5 citations) tackles the practical issue of uneven ground reflectivity measurements from mobile platforms, developing computational methods to create consistent maps despite variable observation densities. These contributions have direct applications in improving the reliability and accuracy of autonomous vehicle navigation systems, making Castorena's work essential reading for researchers and engineers working on sensor fusion, localization, and mapping for self-driving cars.