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About

Charles Freundlich is a researcher in robotics and active perception, whose work focuses on the fundamental challenge of how a mobile robot can intelligently move to best localize targets in its environment. His primary contributions lie at the intersection of optimal path planning, resource allocation, and hybrid control for active target localization using stereo vision. In his most-cited work, "A hybrid control approach to the Next-Best-View problem using stereo vision" (15 citations), he developed a novel method for a mobile robot to precisely localize stationary targets by determining the optimal next camera position. This was extended in "Optimal path planning and resource allocation for active target localization" (14 citations), where he formulated a dynamic programming solution that jointly optimizes robot trajectories and uncertainty reduction. His research is distinguished by its rigorous treatment of real-world sensing constraints, including image quantization noise, and its application to both stationary and mobile targets. Through these contributions, Freundlich has advanced the theory of active perception, providing principled frameworks for robots to autonomously gather the most informative visual data.