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About

Evan Kaufman is a robotics researcher specializing in autonomous aerial exploration and probabilistic mapping. His work focuses on enabling quadrotors and multi-robot systems to intelligently navigate and map unknown three-dimensional environments. Kaufman’s key contribution lies in integrating Bayesian probabilistic occupancy grid mapping with optimal motion planning, allowing drones to minimize map uncertainty during exploration. His 2018 paper on autonomous quadrotor 3D mapping using exact occupancy probabilities (21 citations) and his 2016 work on expected information gain from probabilistic occupancy grids (17 citations) are foundational in this area. He also advanced the field with exact inverse sensor models for autonomous exploration (11 citations) and extended his methods to multi-robot patrol of structured indoor environments (2019). Kaufman’s research has direct applications in search-and-rescue, infrastructure inspection, and environmental monitoring, where efficient, uncertainty-aware mapping is critical. His work is widely cited by researchers developing next-generation autonomous navigation systems.