Alan Hernandez

Papers

Total Citations

1,180

H-Index

About

Alan Hernandez is a leading researcher in robotics and autonomous systems, with a primary focus on visual-inertial state estimation and its application in challenging, real-world environments. His major contributions lie in rigorously evaluating and advancing the robustness of simultaneous localization and mapping (SLAM) algorithms, particularly for domains where traditional methods fail. Hernandez’s most influential work, published at the 2019 IEEE/RSJ IROS conference, has garnered over 1,079 citations, establishing a benchmark for state estimation techniques that integrate visual and inertial data. This paper critically assessed the performance of these algorithms beyond controlled indoor and urban settings, exposing their limitations and driving the field toward more resilient solutions. Expanding on this, his 2019 study on underwater state estimation (101 citations) provided the first systematic experimental comparison of open-source visual-inertial algorithms in the underwater domain, a notoriously difficult environment due to poor lighting and dynamic conditions. This work is notable for bridging a critical gap between land-based robotics and marine applications. Hernandez’s research is essential for enabling reliable autonomy in aerial, ground, and underwater vehicles, making him a key figure in advancing perception systems for real-world deployment.