DeepAI AI Chat
Log In Sign Up

Learning what is above and what is below: horizon approach to monocular obstacle detection

by   Guido de Croon, et al.
Delft University of Technology

A novel approach is proposed for monocular obstacle detection, which relies on self-supervised learning to discriminate everything above the horizon line from everything below. Obstacles on the path of a robot that keeps moving at the same height, will appear both above and under the horizon line. This implies that classifying obstacle pixels will be inherently uncertain. Hence, in the proposed approach the classifier's uncertainty is used for obstacle detection. The (preliminary) results show that this approach can indeed work in different environments. On the well-known KITTI data set, the self-supervised learning scheme clearly segments the road and sky, while application to a flying data set leads to the segmentation of the flight arena's floor.


page 3

page 5

page 7

page 8


From Agile Ground to Aerial Navigation: Learning from Learned Hallucination

This paper presents a self-supervised Learning from Learned Hallucinatio...

SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning

Monocular depth estimation in the wild inherently predicts depth up to a...

Monocular Depth Estimation with Self-supervised Instance Adaptation

Recent advances in self-supervised learning havedemonstrated that it is ...

Self-supervised classification of dynamic obstacles using the temporal information provided by videos

Nowadays, autonomous driving systems can detect, segment, and classify t...

Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization

Pouring is one of the most commonly executed tasks in humans' daily live...

Perspective Aware Road Obstacle Detection

While road obstacle detection techniques have become increasingly effect...

SMARRT: Self-Repairing Motion-Reactive Anytime RRT for Dynamic Environments

This paper addresses the fast replanning problem in dynamic environments...