Three-dimensional Mapping with Augmented Navigation Cost

Unlike most indoor applications, where surfaces are usually human-made, flat, and structured, external environments may be unpredictable as to the types and conditions of the travel surfaces, such as traction characteristics and inclination. Attaining full autonomy in outdoor environments requires a mobile ground robot to perform the fundamental localization and mapping tasks in unfamiliar environments, but with the added challenge of unknown terrain conditions.

In this work we propose a multimodal representation of unknown terrain. The aforementioned representation is based on the prediction of inertial measurements from LiDAR data, regarding speed-invariant inertial signals. Our methodology trains a Convolutional Neural Network (CNN) on recorded LiDAR and IMU data, and learns to predict a navigation cost for previously-unseen terrain patches.