A complex visual navigation task puts an agent in different situations which call for a diverse range of visual perception abilities. For example, to "go to the nearest chair", the agent might need to identify a chair in a living room using semantics, follow along a hallway using vanishing point cues, and avoid obstacles using depth. Therefore, utilizing the appropriate visual perception abilities based on a situational understanding of the visual environment can empower these navigation models in unseen visual environments. We propose to train an agent to fuse a large set of visual representations that correspond to diverse visual perception abilities. To fully utilize each representation, we develop an action-level representation fusion scheme, which predicts an action candidate from each representation and adaptively consolidate these action candidates into the final action. Furthermore, we employ a data-driven inter-task affinity regularization to reduce redundancies and improve generalization. Our approach leads to a significantly improved performance in novel environments over ImageNet-pretrained baseline and other fusion methods.
The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world. In this paper, we present a Visual-Geometric Fusion Network(VGF-Net), a deep network for the fusion analysis of visual/geometric data and the construction of 2.5D height maps for simultaneous drone navigation in novel environments. Given an initial rough height map and a sequence of RGB images, our VGF-Net extracts the visual information of the scene, along with a sparse set of 3D keypoints that capture the geometric relationship between objects in the scene. Driven by the da…
Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation
City University of Hong Kong, Dongguan Campus（香港城市大学东莞校区）；The University of Melbourne（墨尔本大学）；Tsinghua University（清华大学）；Baidu Inc.（百度公司）；University of Chinese Academy of Science（中国科学院大学）；National University of Singapore（新加坡国立大学）
University of Science and Technology of China（中国科学技术大学）；Hong Kong University of Science and Technology（香港科技大学）；Dajiang Innovations Co., Ltd.（大疆创新科技有限公司）
Towards robust and convenient indoor shopping mall navigation, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users. Specifically, we decompose the visual navigation problem into localization and map generation respectively. Given a storefront input image, a novel feature fusion scheme (denoted as FusionNet) is proposed by fusing the distinguishing DNN-based appearance feature and text feature for robust recognition of store brands, which serves for accurate localization. Regarding the map generat…
Autonomous Robots Lab, University of Nevada, Reno（内华达大学雷诺分校自主机器人实验室）
This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused at the feature detection and descriptor extraction levels to augment one sensing modality with the other. These multimodal features are then further integrated with inertial sensor cues using an extended Kalman filter to estimate the robot pose, sensor bias terms, and landmark positions simultaneously as part of the filter state. As demonstrated through a s…
NavEYE: Vision-Centered Multi-Sensor Fusion-Based Situational Awareness System for Intelligent Surface Vehicles
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科教创新园）；School of Navigation, Wuhan University of Technology（武汉理工大学航运学院）；State Key Laboratory of Maritime Technology and Safety（船舶技术与安全国家重点实验室）
Outdoor shopping complexes (OSC) are extremely difficult for people with visual impairment to navigate. Existing GPS devices are mostly designed for roadside navigation and seldom transition well into an OSC-like setting. We report our study on the challenges faced by a blind person in navigating OSC through developing a new mobile application named iExplore. We first report an exploratory study aiming at deriving specific design principles for building this system by learning the unique challenges of the problem. Then we present a methodology that can be used to derive the necessary informati…
University of New Mexico（新墨西哥大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Live fire creates a dynamic, rapidly changing environment that presents a worthy challenge for deep learning and artificial intelligence methodologies to assist firefighters with scene comprehension in maintaining their situational awareness, tracking and relay of important features necessary for key decisions as they tackle these catastrophic events. We propose a deep Q-learning based agent who is immune to stress induced disorientation and anxiety and thus able to make clear decisions for navigation based on the observed and stored facts in live fire environments. As a proof of concept, we i…
Residual Cross-Modal Fusion Networks for Audio-Visual Navigation
School of Computer Science and Technology, Xinjiang University, Urumqi, China（新疆大学计算机科学与技术学院）；Joint International Research Laboratory of Silk Road Multilingual Cognitive（丝绸之路多语认知联合国际实验室）；School of Mechatronic Engineering and Automation Shanghai University, Shanghai, China（上海大学机电工程与自动化学院）
With an ever-widening domain of aerial robotic applications, including many mission critical tasks such as disaster response operations, search and rescue missions and infrastructure inspections taking place in GPS-denied environments, the need for reliable autonomous operation of aerial robots has become crucial. Operating in GPS-denied areas aerial robots rely on a multitude of sensors to localize and navigate. Visible spectrum cameras are the most commonly used sensors due to their low cost and weight. However, in environments that are visually-degraded such as in conditions of poor illumin…
Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Fine-grained visual classification (FGVC) aims to classify sub-classes of objects in the same super-class (e.g., species of birds, models of cars). For the FGVC tasks, the essential solution is to find discriminative subtle information of the target from local regions. TraditionalFGVC models preferred to use the refined features,i.e., high-level semantic information for recognition and rarely use low-level in-formation. However, it turns out that low-level information which contains rich detail information also has effect on improving performance. Therefore, in this paper, we propose cross-lay…
Balgrist University Hospital（巴尔格里斯特大学医院）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）
In surgical navigation, finding correspondence between preoperative plan and intraoperative anatomy, the so-called registration task, is imperative. One promising approach is to intraoperatively digitize anatomy and register it with the preoperative plan. State-of-the-art commercial navigation systems implement such approaches for pedicle screw placement in spinal fusion surgery. Although these systems improve surgical accuracy, they are not gold standard in clinical practice. Besides economical reasons, this may be due to their difficult integration into clinical workflows and unintuitive nav…
Swiss Federal School of Technology in Lausanne - EPFL（洛桑联邦理工学院）；University of Tsukuba（筑波大学）；Swiss Federal School of Technology in Zurich - ETH Zurich（苏黎世联邦理工学院）
Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations. In this work, we present a crowd navigation control framework that delivers continuous obstacle avoidance and post-contact control evaluated on an autonomous personal mobility vehicle. We propose evaluation metrics for accounting efficiency, controller response and crowd interactions in natural crowds. We report the results of over 110 trials in different crowd types: sparse, flows, and mixed traffic, with…
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
Mercedes-Benz Research & Development North America, USA（梅赛德斯-奔驰北美研究与开发）；University of Stuttgart, Germany（斯图加特大学）；University of California, San Diego, USA（加州大学圣地亚哥分校）；University of Southampton, United Kingdom（南安普顿大学）
Interactive Semantic Map Representation for Skill-based Visual Object Navigation
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Artificial Intelligence Research Institute（人工智能研究院）；Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院联邦计算机科学与控制研究中心）
In this work, we propose a new learning approach for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). We develop a multimodal fusion of deep neural architectures for visual-inertial odometry. We train the model in an end-to-end fashion to estimate the current vehicle pose from streams of visual and inertial measurements. We first evaluate the accuracy of our estimation by comparing the prediction of the model to traditional algorithms on the publicly available EuRoC MAV dataset. The results illustrate a $25 \%$ improvement in estimation accuracy over the baseline. Finally…
We present a new four-pronged approach to build firefighter's situational awareness for the first time in the literature. We construct a series of deep learning frameworks built on top of one another to enhance the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. First, we used a deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real-time. Next, we extended this CNN framework for object detection, tracking, segmentation with a Mask RCNN framework, an…
Democritus University of Thrace（色雷斯德谟克利特大学）；Information Technologies Institute, The Centre for Research & Technology, Hellas（信息技术研究所，研究与技术中心，希腊）
This paper tackles the problem of positioning a swarm of UAVs inside a completely unknown terrain, having as objective to maximize the overall situational awareness. The situational awareness is expressed by the number and quality of unique objects of interest, inside the UAVs' fields of view. YOLOv3 and a system to identify duplicate objects of interest were employed to assign a single score to each UAVs' configuration. Then, a novel navigation algorithm, capable of optimizing the previously defined score, without taking into consideration the dynamics of either UAVs or environment, is propos…
Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion
College of Computer Science, Chongqing University（重庆大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）
Navigation Around Unknown Space Objects Using Visible-Thermal Image Fusion
Draper Scholar, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（麻省理工学院航空航天系）；Senior Member of Technical Staff, The Charles Stark Draper Laboratory Inc.（查尔斯·斯塔克·德拉珀实验室公司）；Ford Professor of Engineering, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（麻省理工学院航空航天系）；Professor Post Tenure, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（麻省理工学院航空航天系）
University of New Mexico（新墨西哥大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Sandia National Labs（桑迪亚国家实验室）
Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a fi…
PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation
Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；University of Adelaide（阿德莱德大学）；Wuhan University（武汉大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Jiaotong University（北京交通大学）；AIR Wuxi Innovation Center, Tsinghua University（清华大学无锡创新中心）；Lenovo（联想集团）
NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）；ISTI - CNR（意大利国家研究委员会信息科学与技术研究所）
Vision-and-Language Navigation (VLN) is a challenging task in which an agent needs to follow a language-specified path to reach a target destination. The goal gets even harder as the actions available to the agent get simpler and move towards low-level, atomic interactions with the environment. This setting takes the name of low-level VLN. In this paper, we strive for the creation of an agent able to tackle three key issues: multi-modality, long-term dependencies, and adaptability towards different locomotive settings. To that end, we devise "Perceive, Transform, and Act" (PTA): a fully-attent…
