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Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network [article]

Anh Nguyen, Ngoc Nguyen, Kim Tran, Erman Tjiputra, Quang D. Tran
2020 arXiv   pre-print
In this work, we propose a multimodal fusion approach to address the problem of autonomous navigation in complex environments such as collapsed cites, or natural caves.  ...  Autonomous navigation in complex environments is a crucial task in time-sensitive scenarios such as disaster response or search and rescue.  ...  We then propose a new Navigation Multimodal Fusion Network (NMFNet) that effectively learns the visual perception from sensor fusion and allows the robot to autonomously navigate in complex environments  ... 
arXiv:2007.15945v1 fatcat:aoyrmjgue5fl5jpnx7xsr3jsgm

Autonomous Navigation with Mobile Robots using Deep Learning and the Robot Operating System [article]

Anh Nguyen, Quang Tran
2020 arXiv   pre-print
We describe three main steps to tackle this problem: i) collecting data in simulation environments using ROS and Gazebo; ii) designing deep network for autonomous navigation, and iii) deploying the learned  ...  In this chapter, we present a set of algorithms to train and deploy deep networks for autonomous navigation of mobile robots using the Robot Operation System (ROS).  ...  In 2017, he co-founded AIOZ in Singapore and led the development of "AIOZ AI" until now, which resulted with various types of AI products and high-quality research, especially in robotics and computer  ... 
arXiv:2012.02417v2 fatcat:ih5jvk77i5e4rkhl7qfa3v67e4

Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding [article]

Zhiyu Huang, Chen Lv, Yang Xing, Jingda Wu
2020 arXiv   pre-print
deep neural network with multimodal sensor fusion.  ...  A further ablation study shows that the model with the removal of multimodal sensor fusion or scene understanding pales in the new environment because of the false perception.  ...  Fig. 2 shows the structure of the proposed deep neural network with multimodal sensor fusion for end-to-end autonomous driving and scene understanding.  ... 
arXiv:2005.09202v2 fatcat:fnr5xcrsvvflfikly7tejhvgpu

Exploration of Deep Learning-based Multimodal Fusion for Semantic Road Scene Segmentation

Yifei Zhang, Olivier Morel, Marc Blanchon, Ralph Seulin, Mojdeh Rastgoo, Désiré Sidibé
2019 Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
Effective and robust segmentation in outdoor scene is prerequisite for safe autonomous navigation of autonomous vehicles.  ...  Deep neural networks have been frequently used for semantic scene understanding in recent years.  ...  Robust and accurate scene parsing of outdoor environments paves the way towards autonomous navigation and relationship inference.  ... 
doi:10.5220/0007360403360343 dblp:conf/visapp/ZhangMBSRS19 fatcat:vfcnkhzjcbbjzfqktbidxhi3ii

Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation [article]

Guan-Horng Liu, Avinash Siravuru, Sai Prabhakar, Manuela Veloso, George Kantor
2017 arXiv   pre-print
We also introduce an additional auxiliary loss on the policy network in order to reduce variance in the band of potential multi- and uni-sensory policies to reduce jerks during policy switching triggered  ...  through the visualization of gradients, we show that the learned policies are conditioned on the same latent states representation despite having diverse observations spaces - a hallmark of true sensor-fusion  ...  Multimodal Deep Reinforcement Learning Deep Reinforcement Learning (DRL) Brief Review: We consider a standard Reinforcement Learning (RL) setup, where an agent operates in an environment E.  ... 
arXiv:1705.10422v2 fatcat:xbntkh4tbvctvlzvrcmlfeb7eq

Self-Driving Cars using Genetic Algorithm

Chuttumanil Manu Samuel
2020 International Journal for Research in Applied Science and Engineering Technology  
This paper deals with a 2D unity simulation of an autonomous car learning to drive in a simplified environment containing only lane and static obstacles.  ...  The neural network used in this paper is standard, fully connected, feed forward neural network. The cars are steered by a feed forward Neural Network.  ...  Fayjie et al has applied an approach for obstacle avoidance and navigation in urban environment using Deep Reinforcement learning applied Deep Q network.  ... 
doi:10.22214/ijraset.2020.32200 fatcat:4yjcrz2al5bipjp3sy3kaxoc34

Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion [article]

Peide Cai, Sukai Wang, Yuxiang Sun, Ming Liu
2020 arXiv   pre-print
Recently, with the rise of deep learning, end-to-end control for autonomous vehicles has been well studied.  ...  All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors.  ...  All authors are with The Hong Kong University of Science and Technology, Hong Kong SAR, China (email: pcaiaa@connect.ust.hk; swangcy@connect.ust.hk; sun.yuxiang@outlook.com; eelium@ust.hk).  ... 
arXiv:2005.01935v1 fatcat:ig2kfs54ivf2hajrlla6suq6cq

Semantic scene understanding and traversability estimation for off-road vehicles

Jesús Copado Rodríguez, András Majdik
2021 Zenodo  
These models are usually built using Deep Learning techniques and, in particular, convolutional networks have been demonstrated to work notably well with image related problems.  ...  To operate autonomously and intelligently in unstructured terrain, a robot or vehicle must possess many advanced navigational skills.  ...  Acknowledgement The research in this publication, which was carried in collaboration with Furthermore, I would also like to thank my supervisors at SZTAKI, András László Madjik for his support and Gábor  ... 
doi:10.5281/zenodo.6325960 fatcat:tvlsekwyvvad5cdqdo2t3otilu

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Khaled Bayoudh, Raja Knani, Fayçal Hamdaoui, Abdellatif Mtibaa
2021 The Visual Computer  
In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based  ...  The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision.  ...  They also depend on an unstructured environment for autonomous decision making (e.g., navigation, localization, and environment mapping (SLAM)).  ... 
doi:10.1007/s00371-021-02166-7 pmid:34131356 pmcid:PMC8192112 fatcat:jojwyc6slnevzk7eaiutlmlgfe

Deep Feature-Level Sensor Fusion Using Skip Connections for Real-Time Object Detection in Autonomous Driving

Vijay John, Seiichi Mita
2021 Electronics  
Object detection is an important perception task in autonomous driving and advanced driver assistance systems.  ...  For robust vision-based perception, we propose a deep learning framework for effective sensor fusion of the visible camera with complementary sensors.  ...  The results obtained from perception are used by the autonomous driving systems for navigation and control.  ... 
doi:10.3390/electronics10040424 fatcat:k7nmhc4hu5fo5lcvhbx2ggfloa

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Amira Mimouna, Ihsen Alouani, Anouar Ben Khalifa, Yassin El Hillali, Abdelmalik Taleb-Ahmed, Atika Menhaj, Abdeldjalil Ouahabi, Najoua Essoukri Ben Amara
2020 Electronics  
A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas.  ...  In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception.  ...  Existing Public Multimodal Environment Perception Databases Public multimodal datasets are important for autonomous driving's advancement.  ... 
doi:10.3390/electronics9040560 fatcat:m7xosqzegbgfnabzqzp3n2gagy

Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles [article]

Jiwei Guan, Xi Zheng, Chen Wang, Yipeng Zhou, Alireza Jolfa
2021 arXiv   pre-print
With recent advances in autonomous driving, Voice Control Systems have become increasingly adopted as human-vehicle interaction methods.  ...  To this end, we propose a novel multimodal deep learning classification system to defend against inaudible command attacks.  ...  Such security threats to autonomous driving take the form of malicious interference with navigation, steering, and control in real-life traffic conditions.  ... 
arXiv:2104.09872v3 fatcat:orgbuv7b4reyzpv76s5amtnxzy

Multimodal Deep Reinforcement Learning with Auxiliary Task for Obstacle Avoidance of Indoor Mobile Robot

Hailuo Song, Ao Li, Tong Wang, Minghui Wang
2021 Sensors  
In this work, we propose a novel multimodal DRL method with auxiliary task (MDRLAT) for obstacle avoidance of indoor mobile robot.  ...  is subsequently fed into dueling double deep Q-network to output control commands for mobile robot.  ...  two-stream CNN and then passed through deep neural network to obtain multimodal representation [20] .  ... 
doi:10.3390/s21041363 pmid:33671913 pmcid:PMC7918974 fatcat:gfu7sx6s7jfffbsfxyl2zdonee

Brain-Inspired Intelligent Systems for Daily Assistance

Anastassia Angelopoulou, Jose Garcia-Rodriguez, Epameinondas Kapetanios, Peter M. Roth, Kenneth Revett
2019 Computational Intelligence and Neuroscience  
e fields of machine learning and cognitive computing have been in the last decade revolutionised with neural-inspired algorithms (e.g., deep ANNs and deep RL) and brainintelligent systems that assist in  ...  using multimodal signals (e.g., neural, physiological, and kinematic), and produce autonomous adaptive agencies, which utilise cognitive and affective data, within a social neuroscientific framework.  ...  e fields of machine learning and cognitive computing have been in the last decade revolutionised with neural-inspired algorithms (e.g., deep ANNs and deep RL) and brainintelligent systems that assist in  ... 
doi:10.1155/2019/7597839 pmid:31316557 pmcid:PMC6604466 fatcat:ypmyw66idnea7g2jqb24pkruly

A Survey of Deep Learning Techniques for Mobile Robot Applications [article]

Jahanzaib Shabbir, Tarique Anwer
2018 arXiv   pre-print
Advancements in deep learning over the years have attracted research into how deep artificial neural networks can be used in robotic systems.  ...  This research survey will present a summarization of the current research with a specific focus on the gains and obstacles for deep learning to be applied to mobile robotics.  ...  How Autonomous Robotic Systems Use Deep Learning to Navigate Their Enivornments Autonomous driving in unstructured environments faces many challenges which do not exist in structured environments.  ... 
arXiv:1803.07608v1 fatcat:edwca3yd5fhcri3zkfdfg2o2ju
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