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Learning to Localize in New Environments from Synthetic Training Data

Learning to Localize in New Environments from Synthetic Training Data

9 November 2020
Dominik Winkelbauer
Maximilian Denninger
Rudolph Triebel
ArXivPDFHTML

Papers citing "Learning to Localize in New Environments from Synthetic Training Data"

4 / 4 papers shown
Title
CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot
  Applications
CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications
J. Blumenkamp
Steven D. Morad
Jennifer Gielis
Amanda Prorok
31
4
0
02 May 2024
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks
  with Sparse Gaussian Processes
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Jongseo Lee
Jianxiang Feng
Matthias Humt
M. Müller
Rudolph Triebel
UQCV
48
21
0
20 Sep 2021
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and
  Odometry
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
Noha Radwan
Abhinav Valada
Wolfram Burgard
70
240
0
23 Apr 2018
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D
  Cameras
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
Raul Mur-Artal
Juan D. Tardós
204
5,375
0
20 Oct 2016
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