Evidential grid mapping, from asynchronous LIDAR scans and RGB images, for autonomous driving

Abstract

We propose an evidential fusion algorithm between LIDAR scans and RGB images. LIDAR points are classified as either belonging to the ground, or not, and RGB images are processed by a state-of-the-art convolutional neural network to obtain semantic labels. The results are fused into an evidential grid to assess the drivability of an area met by an autonomous vehicle, while accounting for incoherences over time and between sensors. The dynamic behaviour of potentially moving objects can be estimated from the high-level semantic labels. LIDAR scans and images are not assumed to be acquired at the same time, making the proposed grid mapping algorithm asynchronous. This approach is justified by the need for coping with, at the same time, sensor uncertainties, incoherences of results over time and between sensors, and the need for handling sensor failure. In classical LIDAR/camera fusion, in which LIDAR scans and images are considered to be acquired at the same time (synchronously), the failure of a single sensor leads to the failure of the whole fusion algorithm. On the contrary, the proposed asynchronous approach can be used to fuse contradictory information over time, while allowing the vehicle to operate even in the event of the failure of a single sensor. Experiments on a challenging use case highlight the interest of the method.