Andoni Arruti

Papers

Total Citations

H-Index

About

Andoni Arruti is a researcher whose work lies at the intersection of computer vision, sensor fusion, and mobile robotics. His most cited contribution, "Fusing multiple image transformations and a thermal sensor with Kinect to improve person detection ability" (2013, 17 citations), addresses a critical challenge in human-robot interaction: robustly detecting people in varied and challenging environments. By combining visible-light image transformations with thermal sensing, Arruti demonstrated a practical approach to overcoming the limitations of single-modality sensors, improving detection reliability in low-light or cluttered settings—a key requirement for autonomous systems operating alongside humans. His work on "Robot Trajectories Comparison: A Statistical Approach" (2014) tackles the fundamental yet unresolved problem of benchmarking motion planning algorithms. Rather than proposing a new planner, Arruti contributes a rigorous statistical framework for evaluating trajectory performance, filling a methodological gap in robotics research. While his citation counts reflect a focused, early-career impact, these papers highlight his commitment to solving real-world perception and evaluation problems, making his research valuable for students and engineers developing safer, more perceptive autonomous robots.