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Deep Auxiliary Learning for Visual Localization and Odometry [article]

Abhinav Valada and Noha Radwan and Wolfram Burgard
2018 arXiv   pre-print
achieving competitive performance for visual odometry estimation.  ...  deep learning technique to be on par with, and in some cases outperforms state-of-the-art SIFT-based approaches.  ...  {Deep Auxiliary Learning for Visual Localization and Odometry}, year = {2018} } arXiv:1803.03642v1 [cs.RO] 9 Mar 2018 Deep Auxiliary Learning for Visual Localization and Odometry Abhinav Valada * Noha  ... 
arXiv:1803.03642v1 fatcat:q5dyoxtk7zf2bjbvdevx6udcam

Enhancing Multi-Robot Perception via Learned Data Association [article]

Nathaniel Glaser, Yen-Cheng Liu, Junjiao Tian, Zsolt Kira
2021 arXiv   pre-print
Specifically, each robot is in charge of locally encoding and decoding visual information, and an extensible neural mechanism allows for an uncertainty-aware and context-based exchange of intermediate  ...  In this paper, we address the multi-robot collaborative perception problem, specifically in the context of multi-view infilling for distributed semantic segmentation.  ...  . • We propose an end-to-end learn-able Multi-Agent Infilling Network, MAIN, that (1) extracts spatial features for pairwise comparison, (2) leverages spatial context and matching uncertainty to produce  ... 
arXiv:2107.00769v1 fatcat:3fnqzee4ejampfeoee3l3oa5hu

Unsupervised Learning of Visual Odometry with Depth Warp Constraints

Haibin Shi, Menghao Guo, Zhi Xu, Yuanbin Zou
2019 IOP Conference Series: Materials Science and Engineering  
Visual Odometry (VO) is one of the important components of Visual SLAM system. Some impressive work on the end-to-end deep neural networks for 6-DoF VO has appeared.  ...  We propose two-part cascade network structure to learn depth from binocular image and to infer ego-motion from consecutive frames.  ...  They use the deep learning method on the visual odometry to be very novel.  ... 
doi:10.1088/1757-899x/563/4/042024 fatcat:oeqci6b6srdipmacvnzry5kbq4

Deep auxiliary learning for visual localization using colorization task [article]

Mi Tian, Qiong Nie, Hao Shen, Xiahua Xia
2021 arXiv   pre-print
Visual localization is one of the most important components for robotics and autonomous driving.  ...  To this end, we propose a novel auxiliary learning strategy for camera localization by introducing scene-specific high-level semantics from self-supervised representation learning task.  ...  [11] firstly introduced multitask learning framework for visual localization, odometry estimate and semantic segmentation.  ... 
arXiv:2107.00222v1 fatcat:wv3m72tyorbxjngf6e36277diu

RIO: Rotation-equivariance supervised learning of robust inertial odometry [article]

Caifa Zhou, Xiya Cao, Dandan Zeng, Yongliang Wang
2021 arXiv   pre-print
It reduces the reliance on massive amounts of labeled data for training a robust model and makes it possible to update the model using various unlabeled data.  ...  Adaptive TTT improves models performance in all cases and makes more than 25% improvements under several scenarios.  ...  Unlike Visual-Inertial Odometry (VIO) [8] that is sensitive to surroundings and cannot work under extreme lighting, IMU-only inertial odometry is more desired and possible to perform accurate and robust  ... 
arXiv:2111.11676v1 fatcat:kv4p265btvaozmmx46sythodjy

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction [article]

Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera, Kejie Li, Harsh Agarwal, Ian Reid
2018 arXiv   pre-print
In this paper, we explore the use of stereo sequences for learning depth and visual odometry.  ...  competitive results for visual odometry; (ii) deep feature-based warping loss improves upon simple photometric warp loss for both single view depth estimation and visual odometry.  ...  This work was supported by the UoA Scholarship to HZ and KL, the ARC Laureate Fellowship FL130100102 to IR and the Australian Centre of Excellence for Robotic Vision CE140100016.  ... 
arXiv:1803.03893v3 fatcat:uqdpu4ypafbq3fkieqfpowvhbi

Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

Jonas Kulhanek, Erik Derner, Robert Babuska
2021 IEEE Robotics and Automation Letters  
Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given  ...  To facilitate the training, we propose visual auxiliary tasks and a tailored reward scheme. The policy is fine-tuned on images collected from real-world environments.  ...  CONCLUSION & FUTURE WORK In this paper, we proposed a deep reinforcement learning method for visual navigation in real-world settings.  ... 
doi:10.1109/lra.2021.3068106 fatcat:c7dpcyjhfvbwrj24uvs5ib46bm

Deep Learning for Underwater Visual Odometry Estimation

Bernardo Teixeira, Hugo Silva, Anibal Matos, Eduardo Silva
2020 IEEE Access  
application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation.  ...  INDEX TERMS Artificial intelligence, computer vision, deep learning, visual odometry, robot navigation, visual SLAM.  ...  This method is denoted as Deep Auxiliary Learning.  ... 
doi:10.1109/access.2020.2978406 fatcat:zjjpiqgol5bclksbob6lnrf2lu

Geometric Consistency for Self-Supervised End-to-End Visual Odometry [article]

Ganesh Iyer and J. Krishna Murthy and Gunshi Gupta and K. Madhava Krishna and Liam Paull
2018 arXiv   pre-print
In this work, we propose an unsupervised paradigm for deep visual odometry learning.  ...  With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO  ...  This makes a strong case for using geometric consistency for unsupervised learning, especially in tasks such as visual odometry and SLAM.  ... 
arXiv:1804.03789v1 fatcat:eqi2pzijpbbtpn7vtp4julfc64

Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos

Haimei Zhao, Wei Bian, Bo Yuan, Dacheng Tao
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
Scene perceiving and understanding tasks including depth estimation, visual odometry (VO) and camera relocalization are fundamental for applications such as autonomous driving, robots and drones.  ...  Therefore, we present a collaborative learning framework, consisting of DepthNet, LocalPoseNet and GlobalPoseNet with a joint optimization loss to estimate depth, VO and camera localization unitedly.  ...  Acknowledgments The work is supported by Australian Research Council Projects FL-170100117, IH-180100002, the Natural Science Foundation of China (NSFC)(No.U1713214) and Shenzhen Fundamental Research Fund  ... 
doi:10.24963/ijcai.2020/68 dblp:conf/ijcai/ZhaoBYT20 fatcat:pz5cw3qaqjhyvobynryqpla63a

Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency [article]

Eun Sun Lee, Junho Kim, Young Min Kim
2021 arXiv   pre-print
The transferred agent exhibits improved localization and mapping accuracy, further leading to enhanced performance in downstream visual navigation tasks.  ...  We propose a light-weight, self-supervised adaptation for a visual navigation agent to generalize to unseen environment.  ...  Recent deep-learning based approaches, on the other hand, often train an intelligent agent jointly for mapping and planning [23, 38] .  ... 
arXiv:2110.07184v1 fatcat:unvxebsmyrefrhivtcbjtkebmq

Exploring Self-Attention for Visual Odometry [article]

Hamed Damirchi, Rooholla Khorrambakht, Hamid D. Taghirad
2020 arXiv   pre-print
In this paper, we explore the effectiveness of self-attention in visual odometry. We report qualitative and quantitative results against the SOTA methods.  ...  However, due to the existence of dynamic objects and texture-less surfaces in the scene, the motion information for every image region might not be reliable for inferring odometry due to the ineffectiveness  ...  Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks.  ... 
arXiv:2011.08634v1 fatcat:kvkg42s2xfegvc22twr5orks2e

Geometric Consistency for Self-Supervised End-to-End Visual Odometry

Ganesh Iyer, J. Krishna Murthy, Gunshi Gupta, K. Madhava Krishna, Liam Paull
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In this work, we propose an unsupervised paradigm for deep visual odometry learning.  ...  With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO  ...  This makes a strong case for using geometric consistency for unsupervised learning, especially in tasks such as visual odometry and SLAM.  ... 
doi:10.1109/cvprw.2018.00064 dblp:conf/cvpr/IyerMGKP18 fatcat:ppvwcekapjcqnbt65omqocwlvi

Project Milou: Vision Based Autonomous Snowplow

Chude Qian, Kaiyi Chen, Ziyi Huang, Drew Borneman
2020 Zenodo  
This is the poster for poster presentation session at the 2020 ION Autonomous Snowplow Competition, hosted in Dunwoody College of Technology, Minneapolis, MN.  ...  The previously used ultra wide band localization system has been replaced by visual odometry and visual localization to achieve better flexibility in the robot's operating environment.  ...  Odometry (D435) RGB Front Camera View Visual IMU (T265) Fused Odometry Trajectory Genertor Goal Pose Actioin Obstacle Information YoloV3 Obstacle Model will be needed regardless  ... 
doi:10.5281/zenodo.3660105 fatcat:dtqnwiujxvfhzkkhtgc3oukequ

DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination [article]

Muhamad Risqi U. Saputra, Pedro P.B. de Gusmao, Chris Xiaoxuan Lu, Yasin Almalioglu, Stefano Rosa, Changhao Chen, Johan Wahlström, Wei Wang, Andrew Markham, Niki Trigoni
2020 arXiv   pre-print
To overcome this issue, we propose a Deep Neural Network model for thermal-inertial odometry (DeepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary  ...  Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features.  ...  For deep learning based approaches, we use VINet [23] which fuses IMU and visual features in the intermediate layer.  ... 
arXiv:1909.07231v2 fatcat:w4jtfllubvfsbl6a6lq3qnjxe4
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