In this paper we propose an on-line system that discovers and drives collision-free traversable paths, using a variational approach to dense stereo vision. Our system is light weight, can be run on low cost hardware and is remarkably quick to predict the semantics. In addition to the scene's path affordance it yields a segmentation of the local scene as a composite of distinctive labels -e.g, ground, sky, obstacles and vegetation. To estimate the labels, we combine a very fast and light weight (shallow) image classifier which considers informative feature channels derived from colour images and dense depth maps estimates. Unlike other approaches, we do not use local descriptors around pixel features. Instead, we encompass label-predicted probabilities with a variational approach for image segmentation. Akin to dense depth map estimation, we obtain semantically segmented images by means of convex regularisation. We show how our system can rapidly obtain the required semantics and paths at VGA resolution. Extensive experiments on the KITTI dataset support the robustness of our system to derive collision-free local routes. An accompanied video supports the robustness of the system at live execution in an outdoor experiment.
Over the last decade, the development of Unmanned Ground Vehicles (UGVs) has received significant attention through technology competitions, such as the DARPA Grand Challenges, where an unmanned vehicle autonomously navigated across a desert or through semiurban roads. Although this marked a significant step forward in autonomous navigation, the current generation of UGVs are only capable of operating in controlled environments where the dynamics of a scene are wellunderstood. For example in both of the DARPA Grand Challenges, the participants were given detailed maps and the environment was carefully controlled during the competition. The next generation of UGVs will need to operate in uncontrolled environments where the terrain and infrastructure is uncertain and humans could be present. This paper discusses the challenges and current developments in the areas of sensing, localisation and planning to realise the next generation of UGVs.
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