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The equations of motion governing mobile robots are dependent on terrainproperties such as the coefficient of friction, and contact model parameters.Estimating these properties is thus essential for robotic navigation. Ideallyany map estimating terrain properties should run in real time, mitigate sensornoise, and provide probability distributions of the aforementioned properties,thus enabling risk-mitigating navigation and planning. This paper addressesthese needs and proposes a Bayesian inference framework for semantic mappingwhich recursively estimates both the terrain surface profile and a probabilitydistribution for terrain properties using data from a single RGB-D camera. Theproposed framework is evaluated in simulation against other semantic mappingmethods and is shown to outperform these state-of-the-art methods in terms ofcorrectly estimating simulated ground-truth terrain properties when evaluatedusing a precision-recall curve and the Kullback-Leibler divergence test.Additionally, the proposed method is deployed on a physical legged roboticplatform in both indoor and outdoor environments, and we show our methodcorrectly predicts terrain properties in both cases. The proposed frameworkruns in real-time and includes a ROS interface for easy integration.

These Maps Are Made For Walking: Real-Time Terrain Property Estimation for Mobile Robots