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

Parker Ewen, Adam Li, Yuxin Chen, Steven Hong, Ram Vasudevan

Abstract:

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.