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Deep line detection for autonomous navigation
Learning-based robust line detection algorithm for autonomous navigation.
Keywords: Line detection, Autonomous Navigation, Deep Learning for Robotics
Detecting lines is necessary to achieve robust autonomous navigation in a number of different scenarios such as autonomous driving and drone inspection.
Most of the works in autonomous driving detect lines from the segmentation of the entire scene. Although these approaches achieve high accuracy, they are computationally demanding. Consequently they cannot be used on resource-constrained platforms as quadrotors.
Contrary, cheaper computationally methods rely on object-detection capabilities to detect the lines but they do not exploit prior knowledge regarding the line shape and parameterization.
In this project, inspired by state-of-the-art deep networks designed for object detection, we will design a light-weight algorithm that exploits prior knowledge on the line parameterization to achieve robust detections.
The goal of the project is to develop an approach that is light-weight and runs in different scenarios, e.g.drone inspection and autonomous driving.
A successful thesis will lead to the deployment of the developed algorithm on a real quadrotor platform for power-line inspection.
Requirements: - Hands-on experience with deep learning - Passionate about robotics - Programming skills in python and deep learning softwares (e.g. Pytorch and / or Tensorflow).
Detecting lines is necessary to achieve robust autonomous navigation in a number of different scenarios such as autonomous driving and drone inspection. Most of the works in autonomous driving detect lines from the segmentation of the entire scene. Although these approaches achieve high accuracy, they are computationally demanding. Consequently they cannot be used on resource-constrained platforms as quadrotors. Contrary, cheaper computationally methods rely on object-detection capabilities to detect the lines but they do not exploit prior knowledge regarding the line shape and parameterization. In this project, inspired by state-of-the-art deep networks designed for object detection, we will design a light-weight algorithm that exploits prior knowledge on the line parameterization to achieve robust detections. The goal of the project is to develop an approach that is light-weight and runs in different scenarios, e.g.drone inspection and autonomous driving. A successful thesis will lead to the deployment of the developed algorithm on a real quadrotor platform for power-line inspection. Requirements: - Hands-on experience with deep learning - Passionate about robotics - Programming skills in python and deep learning softwares (e.g. Pytorch and / or Tensorflow).
In this project we will develop a learning-based robust line detection algorithm for autonomous navigation. Nice-to-have: deployment on a real robotic platform.
In this project we will develop a learning-based robust line detection algorithm for autonomous navigation. Nice-to-have: deployment on a real robotic platform.
Giovanni Cioffi [cioffi (at) ifi (dot) uzh (dot) ch], Daniel Gehrig [dgehrig (at) ifi (dot) uzh (dot) ch]
Giovanni Cioffi [cioffi (at) ifi (dot) uzh (dot) ch], Daniel Gehrig [dgehrig (at) ifi (dot) uzh (dot) ch]
