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Deep Auxiliary Learning for Visual Localization and Odometry
[article]
2018
arXiv
pre-print
Preserved Fulltext
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]
2021
arXiv
pre-print
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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
2019
IOP Conference Series: Materials Science and Engineering
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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]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2021
IEEE Robotics and Automation Letters
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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
2020
IEEE Access
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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]
2018
arXiv
pre-print
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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
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Preserved Fulltext
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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
2020
Zenodo
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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]
2020
arXiv
pre-print
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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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