Mobile robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths and to react to unforeseen terrain patterns. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific wheeled robot model (but may implement any other locomotion type such as tracked, legged, snake-like), using simulation data on a variety of procedurally generated training terrains; once trained, the classifier can quickly be applied to patches extracted from unseen large heightmaps, in multiple orientations, thus building oriented traversability maps. We quantitatively validate the approach on real-elevation datasets and implement a path planning approach that employs our traversability estimation.
A key feature for an autonomous mobile robot navigating in off-road unknown areas is environment sensing. Extraction of meaningful information from sensor data allows a good characterization of the near to far terrains, and thus, the ability for the vehicle to achieve its tasks with easiness. We present an image feature extraction scheme to predict mobile platform motion information. For a sequence of run, several images of terrains and vibrations endured by the mobile robot are acquired using a camera and an acceleration sensor. Texture information extracted by the Segmentation-based Fractal Texture Analysis descriptor (SFTA) was used to find correlations with acceleration features quantified using different time analysis parameters. Experimental results showed that texture information is a good candidate to predict running information. Real time estimation of soil parameters is essential in achieving precise, robust autonomous guidance and control of a tracked vehicle. The paper shows that the slip of the tracks over the terrain can be identified from trajectory data using an extended Kalman filter. The use of a suitable soil model can then allow key soil parameters to be estimated as the vehicle passes over the soil. Knowledge of the soil parameters may in turn be used to allow reference trajectories and control algorithms to be adjusted to suit the soil conditions.