Ben Herbst

Papers

Total Citations

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About

Ben Herbst’s research centers on mobile robotics, with a particular focus on dense mapping, sensor fusion, and pose estimation under uncertainty. His major contribution lies in addressing the critical challenge of integrating range measurements into consistent world-centric occupancy grids when a robot’s pose is uncertain. In his most-cited work, “Pose Uncertainty in Occupancy Grids through Monte Carlo Integration” (2014, 7 citations), Herbst introduced a novel Monte Carlo-based framework that explicitly accounts for pose uncertainty during map construction, significantly improving the robustness of occupancy grids in real-world SLAM applications. This approach, refined in a 2013 companion paper (4 citations), demonstrates how probabilistic integration can mitigate errors from noisy odometry and sensor data. While his citation counts reflect a focused, early-career impact, Herbst’s work is notable for its practical relevance—bridging theoretical uncertainty quantification with deployable robotic mapping systems. His contributions are particularly valuable for researchers and students working on autonomous navigation, where reliable mapping under pose drift remains a fundamental hurdle. Herbst’s method offers a clear, computationally tractable path to more resilient spatial representations.