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