Inferring Depth Maps from 2-Dimensional Laser Ranging Data in a Simulated Environment
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Sähkötekniikan korkeakoulu |
Master's thesis
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Authors
Date
2018-12-17
Department
Major/Subject
Control, Robotics and Autonomous Systems
Mcode
ELEC3025
Degree programme
AEE - Master’s Programme in Automation and Electrical Engineering (TS2013)
Language
en
Pages
39+10
Series
Abstract
Depth estimation plays a key role in mobile robotics for applications including scene understanding, navigation and mapping. Recently, deep learning methods have proven effective in estimating depth maps from a combination of different sources such as 3D LiDAR or RGB images. However, they face two challenges; the lack of dense ground truth data and the depth input sparsity, which ranges from 4-10% pixel density on an input image. This thesis explores the feasibility of inferring a full depth map from extremely sparse 2D LiDAR measurements via neural network. To address the lack of ground truth data, a simulation tool is created for data gathering. The results show that from our sparse input of 0.024% pixel density on input images, the tested network infers shapes but struggles with blurry boundaries on objects.Description
Supervisor
Kyrki, VilleThesis advisor
Verdoja, FrancescoLundell, Jens
Keywords
depth completion, deep learning, robotic simulation, convolutional neural networks