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The focus of this contribution is on camera simulation as it comes into playin simulating autonomous robots for their virtual prototyping. We propose acamera model validation methodology based on the performance of a perceptionalgorithm and the context in which the performance is measured. This approachis different than traditional validation of synthetic images, which is oftendone at a pixel or feature level, and tends to require matching pairs ofsynthetic and real images. Due to the high cost and constraints of acquiringpaired images, the proposed approach is based on datasets that are notnecessarily paired. Within a real and a simulated dataset, A and B,respectively, we find subsets Ac and Bc of similar content and judge,statistically, the perception algorithm's response to these similar subsets.This validation approach obtains a statistical measure of performancesimilarity, as well as a measure of similarity between the content of A and B.The methodology is demonstrated using images generated with Chrono::Sensor anda scaled autonomous vehicle, using an object detector as the perceptionalgorithm. The results demonstrate the ability to quantify (i) differencesbetween simulated and real data; (ii) the propensity of training methods tomitigate the sim-to-real gap; and (iii) the context overlap between twodatasets.

A performance contextualization approach to validating camera models for robot simulation