Marco Barrera

Papers

Total Citations

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About

Marco Barrera’s research focuses on advancing robotic perception and localization, particularly for computationally constrained systems like planetary rovers. His work centers on pose estimation, visual odometry, and sensor calibration—critical components for autonomous navigation in challenging environments. Barrera’s most cited paper, “Viewpoint Selection for Rover Relative Pose Estimation Driven by Minimal Uncertainty Criteria” (2021, 12 citations), introduces a novel approach to reducing pose estimation uncertainty by strategically selecting viewpoints, enabling rovers to maintain accurate localization without relying on computationally heavy SLAM algorithms. This work directly addresses the trade-off between precision and resource efficiency in space robotics. In “Camera Rig Extrinsic Calibration Using a Motion Capture System” (2018, 7 citations), Barrera provides a reliable method for calibrating multi-camera setups using motion capture systems, offering ground truth trajectories essential for validating visual odometry and SLAM algorithms during development. His contributions have practical implications for planetary exploration missions, where limited computational power demands innovative solutions. With a growing citation impact, Barrera’s research bridges the gap between theoretical uncertainty minimization and real-world robotic deployment, making him a notable figure in field robotics and autonomous navigation.