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Topometric Localization with Deep Learning
[article]
2017
arXiv
pre-print
Preserved Fulltext
In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an accuracy ...
that is on-par with LiDAR-based localization. ...
Methodology This paper proposes a topometric localization method using image sequences from a camera with a deep learning approach. ...
arXiv:1706.08775v1
fatcat:byrfl5wxmvcfbgdj3rawrm5inq
Trajectory Prediction for Autonomous Driving with Topometric Map
[article]
2021
arXiv
pre-print
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The proposed model takes raw LiDAR data and noisy topometric map as input and produces precise local trajectory for navigation. ...
The experimental results show that the proposed method outperforms state-of-the-art multimodal methods and is robust to the perturbations of the topometric map. ...
Deep learning-based trajectory prediction The success of deep learning in many real-life applications prompts research on trajectory prediction. ...
arXiv:2105.03869v1
fatcat:cpyxcd55zbbxlljxx4wfpkj4ae
Spatial Concept-based Topometric Semantic Mapping for Hierarchical Path-planning from Speech Instructions
[article]
2022
arXiv
pre-print
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The navigation experiment using human speech instruction shows that the proposed spatial concept-based hierarchical path planning improves the performance and reduces the calculation cost compared with ...
We propose a novel probabilistic generative model, SpCoTMHP, that forms a topometric semantic map that adapts to the environment and leads to hierarchical path planning. ...
In addition, recent studies on vision-and-language navigation have used deep and reinforcement learning [19] , [20] . ...
arXiv:2203.10820v1
fatcat:tsqwersvtvby5a2oeuaxanl35i
Learning Topometric Semantic Maps from Occupancy Grids
[article]
2020
arXiv
pre-print
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We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a random-sized map. ...
Today's mobile robots are expected to operate in complex environments they share with humans. ...
The authors are with the Institute of Information Technology, Department of Electrical, Electronic and Communication Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany; Correspondence ...
arXiv:2001.03676v1
fatcat:gpms6d4csbdi3a3tpsb3rbbe2i
Deep-Geometric 6 DoF Localization from a Single Image in Topo-metric Maps
[article]
2020
arXiv
pre-print
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With potential VR/AR and localization applications in single camera devices such as mobile phones and drones, our hybrid algorithm compares favourably with the fully Deep-Learning based Pose-Net that regresses ...
During the localization phase, we take a single camera image, localize it to a topological node using Deep Learning, and use a geometric algorithm (PnP) on the matched 2D features (and their 3D positions ...
Deep-Geometric Localization Our localizer is a hybrid between newer Deep Learning techniques and geometric computer vision methods. ...
arXiv:2002.01210v1
fatcat:vp54ivbokngwlcchtizb7jiioq
Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?
[article]
2022
arXiv
pre-print
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We present an extensive comparison between three topometric localization systems: radar-only, lidar-only, and a cross-modal radar-to-lidar system across varying seasonal and weather conditions using the ...
However, our radar-only pipeline was able to achieve competitive accuracy with a much smaller map. ...
Barnes and Posner [32] demonstrated radar odometry using deep learned features and a differentiable singular value decomposition (SVD)-based estimator. ...
arXiv:2203.10174v2
fatcat:any3gc26szafjkdaswnj4lp75e
Guest Editorial: Special issue on "Topological methods in robotics"
2021
Autonomous Robots
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With advances in persistent homology algorithms, topological data analysis techniques have been used on large data sets (such as 3D point clouds in context of visual mapping) and sensor networks for identification ...
The next article, "Partial caging: a clearance-based definition, datasets, and deep learning" by Welle et al., also uses machine learning techniques in conjunction with topological reprsentations and abstractions ...
The paper "Hierarchical Topometric Repre-
sentation of
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
doi:10.1007/s10514-021-09989-2
fatcat:vylcouyngfc73f5j352wzdwrrm
Mining Minimal Map-Segments for Visual Place Classifiers
[article]
2019
arXiv
pre-print
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The proposed map representation was implemented with three types of VPC: deep convolutional neural network, bag-of-words, and object class detector, and each was integrated into a Monte Carlo localization ...
algorithm (MCL) within a topometric VPR framework. ...
., learning) and localization (i.e., prediction) as two separate processes. ...
arXiv:1909.09594v1
fatcat:jhvooktx6zes3n6qelcwaal6dy
Efficient and Robust LiDAR-Based End-to-End Navigation
[article]
2021
arXiv
pre-print
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Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input. ...
Instead, evidential deep learning aims to directly learn the underlying epistemic uncertainty distribution using a neural network to estimate uncertainty without the need for sampling [42] , [43] . ...
Furthermore, recent works have demonstrated the ability to perform point-to-point navigation using only coarse localization and sparse topometric maps [5] , [6] , without the need for pre-collected highdefinition ...
arXiv:2105.09932v1
fatcat:gc7siqnm3jaj5kkozgpi75qftm
DPC-Net: Deep Pose Correction for Visual Localization
2018
IEEE Robotics and Automation Letters
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2018 pre-print arXiv:1709.03128v2 fatcat:g2sg2dssrbd2nc5wbuqhnjskcqOther Versions
We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. ...
In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections ...
Index Terms-Deep Learning in Robotics and Automation, Localization
I. ...
doi:10.1109/lra.2017.2778765
dblp:journals/ral/PeretroukhinK18
fatcat:jdp7zrnrvbbdtoxsghos2axrai
DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network
[article]
2020
arXiv
pre-print
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pose for visual odometry (VO) and for localization with respect to a path. ...
We leverage multi-experience VT\&R together with two datasets of outdoor driving on two separate paths spanning different times of day, weather, and seasons to teach a deep neural network to predict relative ...
in a survey on deep learning and visual simultaneous localization and mapping (SLAM) [19] . ...
arXiv:2003.02946v1
fatcat:doezd3kn5vfkzfliv72mtvogue
Monocular Visual Teach and Repeat Aided by Local Ground Planarity
[chapter]
2016
Springer Tracts in Advanced Robotics
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Existing implementations of VT\&R typically rely on 3D sensors such as stereo cameras for mapping and localization, but many mobile robots are equipped with only 2D monocular vision for tasks such as teleoperated ...
In this paper, we propose a monocular vision pipeline that enables kilometre-scale route repetition with centimetre-level accuracy by approximating the ground surface near the vehicle as planar (with some ...
Acknowledgements The authors would like to thank Matthew Giamou and Valentin Peretroukhin of the Space and Terrestrial Autonomous Robotic Systems (STARS) lab for their assistance with field testing, the ...
doi:10.1007/978-3-319-27702-8_36
fatcat:gqxzo2v62rc4zblx4chvpr3bxq
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change
2018
IEEE Robotics and Automation Letters
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2018 pre-print arXiv:1709.03009v4 fatcat:wwv6mblyqrhlxpzrf2y25kkhseOther Versions
Direct visual localization has recently enjoyed a resurgence in popularity with the increasing availability of cheap mobile computing power. ...
We further provide a preliminary investigation of transfer learning from synthetic to real environments in a localization context. ...
Techniques such as [5] , [6] learn end-to-end estimators using deep learning, while others such as [18] , [19] combine deep models with traditional estimation machinery. ...
doi:10.1109/lra.2018.2799741
dblp:journals/ral/ClementK18
fatcat:oiikdehmazcc7gpch6gxyqfb3i
Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
[article]
2020
arXiv
pre-print
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In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descriptors, domain translation, and sequential filtering, all with shortcomings including ...
In this paper we propose a novel descriptor derived from tracking changes in any learned global descriptor over time, dubbed Delta Descriptors. ...
With the advent of deep learning, hand-crafted descriptors such as SIFT [1] have largely been replaced by learned descriptors such as LIFT [9] and DeLF [10] . ...
arXiv:2006.05700v1
fatcat:otkwqvk6ire43lkb6zc35hel5y
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
[article]
2021
arXiv
pre-print
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However, these systems often assume that the car is accurately localized against a high-definition map. ...
Based on our observations, we design a system that jointly performs perception, prediction, and localization. ...
: We approach localization using ground intensity localization with deep LiDAR embeddings [9] . ...
arXiv:2101.06720v3
fatcat:5gczf7qldfdbvmnstpfgru7jtq
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