  Obstacle Detection is a central problem for any robotic system, and critical
for autonomous systems that travel at high speeds in unpredictable environment.
This is often achieved through scene depth estimation, by various means. When
fast motion is considered, the detection range must be longer enough to allow
for safe avoidance and path planning. Current solutions often make assumption
on the motion of the vehicle that limit their applicability, or work at very
limited ranges due to intrinsic constraints. We propose a novel
appearance-based Object Detection system that is able to detect obstacles at
very long range and at a very high speed (~300Hz), without making assumptions
on the type of motion. We achieve these results using a Deep Neural Network
approach trained on real and synthetic images and trading some depth accuracy
for fast, robust and consistent operation. We show how photo-realistic
synthetic images are able to solve the problem of training set dimension and
variety typical of machine learning approaches, and how our system is robust to
massive blurring of test images.
