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Peter Cheeseman is a pioneering figure in robotics and artificial intelligence, best known for his foundational work on spatial uncertainty and probabilistic reasoning. His research centers on the representation and estimation of uncertain spatial relationships, a critical challenge in autonomous robotics. Cheeseman’s most influential contribution is the development of the stochastic map, a probabilistic framework that allows robots to estimate the relative locations of objects and their associated errors, even when those relationships are only known indirectly. His landmark 1986 paper, “On the Representation and Estimation of Spatial Uncertainty,” has garnered over 1,560 citations, while his subsequent work on the stochastic map has accumulated thousands more, cementing his impact on the field. Beyond spatial reasoning, Cheeseman has explored time representation for automatic planning and induction under uncertainty using Bayesian methods. His work has been instrumental in advancing mobile robotics, enabling more reliable navigation and mapping in uncertain environments. For students and researchers, Cheeseman’s contributions remain essential reading in probabilistic robotics and autonomous systems.