International audience—The accurate detection and classification of moving objects is a critical aspect of Advanced Driver Assistance Systems (ADAS). We believe that by including the objects classification from multiple sensors detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second , we propose a complete perception fusion architecture based on the Evidential framework to solve the Detection and Tracking of Moving Objects (DATMO) problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project which includes three main sensors: radar, lidar and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car and truck

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. 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 robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six realworld elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a longrange sensor (such as a camera); we assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information-rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.

We introduce a novel approach to long-range path planning that relies on a learned model to predict the outcome of local motions using possibly partial knowledge. The model is trained from a dataset of trajectories acquired in a self-supervised way. Sampling-based path planners use this component to evaluate edges to be added to the planning tree. We illustrate the application of this pipeline with two robots: a complex, simulated, quadruped robot (ANYmal) moving on rough terrains; and a simple, real, differential-drive robot (Mighty Thymio), whose geometry is assumed unknown, moving among obstacles. We quantitatively evaluate the model performance in predicting the outcome of short moves and long-range paths; finally, we show that planning results in reasonable paths. Index Terms-Motion and path planning, deep learning in robotics and automation, probability and statistical methods.