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Topometric Localization with Deep Learning [article]

Gabriel L. Oliveira, Noha Radwan, Wolfram Burgard, Thomas Brox
2017 arXiv   pre-print
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]

Jiaolong Xu, Liang Xiao, Dawei Zhao, Yiming Nie, Bin Dai
2021 arXiv   pre-print
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]

Akira Taniguchi, Shuya Ito, Tadahiro Taniguchi
2022 arXiv   pre-print
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]

Markus Hiller, Chen Qiu, Florian Particke, Christian Hofmann and Jörn Thielecke
2020 arXiv   pre-print
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]

Tom Roussel, Punarjay Chakravarty, Gaurav Pandey, Tinne Tuytelaars, Luc Van Eycken
2020 arXiv   pre-print
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]

Keenan Burnett, Yuchen Wu, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot
2022 arXiv   pre-print
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"

Subhrajit Bhattacharya, Florian T. Pokorny, Howie Choset
2021 Autonomous Robots  
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]

Tanaka Kanji
2019 arXiv   pre-print
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]

Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, Daniela Rus
2021 arXiv   pre-print
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

Valentin Peretroukhin, Jonathan Kelly
2018 IEEE Robotics and Automation Letters  
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]

Mona Gridseth, Timothy D. Barfoot
2020 arXiv   pre-print
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]

Lee Clement, Jonathan Kelly, Timothy D. Barfoot
2016 Springer Tracts in Advanced Robotics  
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

Lee Clement, Jonathan Kelly
2018 IEEE Robotics and Automation Letters  
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]

Sourav Garg, Ben Harwood, Gaurangi Anand, Michael Milford
2020 arXiv   pre-print
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]

John Phillips, Julieta Martinez, Ioan Andrei Bârsan, Sergio Casas, Abbas Sadat, Raquel Urtasun
2021 arXiv   pre-print
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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