Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks

TL;DR

This paper presents a fast, robust monocular depth estimation system using fully convolutional neural networks, capable of obstacle detection at long range and high speed without motion assumptions, suitable for autonomous robots.

Contribution

It introduces a novel appearance-based obstacle detection method that operates at ~300Hz, leveraging synthetic training data to enhance robustness and range.

Findings

Detects obstacles at very long range and high speed (~300Hz)

Robust to image blurring and variations in environment

Uses synthetic images to improve training diversity

Abstract

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.