In this demo we present INSIGHT, a system that provides traffic event detection in Dublin by exploiting Big Data and Crowdsourcing techniques. Our system is able to process and analyze input from multiple heterogeneous urban data sources.

Environment awareness through advanced sensing systems is a major requirement for a mobile robot to operate safely, particularly when the environment is unstructured, as in an outdoor setting. In this paper, a multi-sensory approach is proposed for automatic traversable ground detection using 3D range sensors. Specifically, two classifiers are presented, one based on laser data and one based on stereovision. Both classifiers rely on a self-learning scheme to detect the general class of ground and feature two main stages: an adaptive training stage and a classification stage. In the training stage, the classifier learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers is statistically combined exploiting their individual advantages in order to reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in a rural environment, are presented to validate this approach, showing its effectiveness for autonomous safe navigation.