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RNR-Nav: A Real-World Visual Navigation System Using Renderable Neural Radiance Maps

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RNR-Nav: A Real-World Visual Navigation System Using Rendera...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Minsoo Kim Obin Kwon Howoong Jun Songhwai Oh Interdisciplinary Program in Artificial Intelligence (IPAI) and ASRI Seoul Republic of Korea Department of Electrical and Computer Enginnering (ECE) and ASRI Seoul National University Seoul Republic of Korea Sequor Robotics Inc. Seoul Republic of Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We propose a novel visual localization and navigation framework for real-world environments directly integrating observed visual information into the bird-eye-view map. While the renderable neural radiance map (RNR-Map) [1] shows considerable promise in simulated settings, its deployment in real-world scenarios poses undiscovered challenges. RNR-Map utilizes projections of multiple vectors into a single latent code, resulting in information loss under suboptimal conditions. To address such issues, our enhanced RNR-Map for real-world robots, RNR-Map++, incorporates strategies to mitigate information loss, such as a weighted map and positional encoding. For robust real-time localization, we integrate a particle filter into the correlation-based localization framework using RNR-Map++ without a rendering procedure. Consequently, we establish a real-world robot system for visual navigation utilizing RNR-Map++, which we call "RNR-Nav." Experimental results demonstrate that the proposed methods significantly enhance rendering quality and localization robustness compared to previous approaches. In real-world navigation tasks, RNR-Nav achieves a success rate of 84.4%, marking a 68.8% enhancement over the methods of the original RNR-Map paper.

关键词： Location awareness Visualization Navigation Rendering (computer graphics) Particle filters Encoding Vectors Robustness Real-time systems Intelligent robots

Adaptive Robot-Mediated Assessment using LLM for Enhanced Survey Quality in Older Adults Care programs 25

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Adaptive Robot-Mediated Assessment using LLM for Enhanced Su...

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Cheonshu Park Miyoung Cho Minjung Shin Jeh-Kwang Ryu Minsu Jang Social Robotics Research Lab Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea Interdisciplinary Program in Cognitive Science Seoul National University Seoul Republic of Korea Department of Physical Education Dongguk University Seoul Republic of Korea

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

关键词： Surveys Large language models Human-robot interaction Medical services Virtual reality Particle measurements Real-time systems Question generation Older adults Robots

AllWeather-UsableWearable Dual Energy Harvester for Outdoor Sustainable Operation

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SusMat 2025年第1期5卷 159-170页

作者： Yeongju Jung Jiyong Ahn Ji-Seok Kim Junhyuk Bang Minwoo Kim Seongmin Jeong Jinwoo Lee Seonggeun Han Il-Kwon Oh Seung Hwan Ko Applied Nano and Thermal Science Lab Department of Mechanical EngineeringSeoul National UniversitySeoulRepublic of Korea National Creative Research Initiative for Functionally Antagonistic Nano-Engineering Department of Mechanical EngineeringSchool of Mechanical and Aerospace EngineeringKorea Advanced Institute of Science and Technology(KAIST)DaejeonRepublic of Korea Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoulRepublic of Korea Device Solutions Research Samsung ElectronicsHwaseong-siRepublic of Korea Institute of Engineering Research/Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National UniversitySeoulRepublic of Korea Interdisciplinary Program in Bioengineering Seoul National UniversitySeoulRepublic of Korea

Sustainable power sources for outdoor wearable electronics are essential for the continuous operation of wearable ***,the current lack of engineering design that can harvest energy regardless of weather conditions presents a significant *** this regard,this study introduces a wearable,breathable all-weather usable dual energy harvester(AWuDEH)that can seamlessly generate electrical energy regardless of weather *** this study,the AWuDEH integrated with the thermoelectric generator and the droplet-based electricity generator is *** AWuDEH,especially engineered with a bi-functional top substrate for radiative cooling and electrification,achieves sustainable energy harvesting outdoors,thereby addressing the conventional challenge associated with the necessity for separate energy harvesters tailored to outdoor usage contingent on weather *** device reaches a maximum power output of 14.6μW cm^(-2)under simulated sunny conditions and generates a much more enhanced thermoelectric power of 74.78μW cm^(-2)and a droplet-based electric power of 256.25 mW m^(-2)in rainy *** proof,this study developed self-powered wearable electronics capable of acquiring physiological signals in simulated outdoor *** study presents a promising advancement in wearable technology,offering a potent solution for sustainable energy harvesting independent of weather conditions.

关键词： all-weather usable droplet-based electricity generator hybrid architecture outdoor sustainable operation thermoelectric generator

Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

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Renderable Street View Map-Based Localization: Leveraging 3D...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

Self-propulsion via slipping: frictional swimming in multi-legged locomotors

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arXiv 2022年

作者： Chong, Baxi He, Juntao Li, Shengkai Erickson, Eva Diaz, Kelimar Wang, Tianyu Soto, Daniel Goldman, Daniel I. Interdisciplinary Graduate Program in Quantitative Biosciences United States School of Physics United States Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA United States

Locomotion is typically studied either in continuous media where bodies and legs experience forces generated by the flowing medium, or on solid substrates dominated by friction. In the former, centralized coordination is believed to facilitate appropriate slipping through the medium for propulsion. In the latter, slip is often assumed minimal and thus avoided via decentralized controls. We discover in laboratory experiments that terrestrial locomotion of a meter scale multi-segmented/legged robophysical model resembles undulatory fluid swimming. Experiments varying waves of limb stepping and body bending reveal how these parameters result in effective terrestrial locomotion despite seemingly ineffective isotropic frictional contacts. Dissipation dominates over inertial effects in this macroscopic-scaled regime, resulting in essentially geometric locomotion akin to microscopic-scale swimming. Theoretical analysis demonstrates that the high-dimensional multi-segmented/legged dynamics can be simplified to a centralized low-dimensional model, which reveals an effective Resistive Force Theory with an acquired viscous drag anisotropy. We extend our low-dimensional, geometric analysis to illustrate how body undulation can aid performance in non-flat obstacle-rich terrains and also use the scheme to quantitatively model how body undulation affects performance of biological centipede locomotion (the desert centipede S. polymorpha) moving at relatively high speeds (∼ 0.5 body lengths/sec). Our results could facilitate control of multilegged robots in complex terradynamic scenarios. © 2022, CC BY.

关键词： Friction

A Portable Multiscopic Camera for Novel View and Time Synthesis in Dynamic Scenes

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arXiv 2022年

作者： Zhang, Tianjia Lau, Yuen-Fui Chen, Qifeng The Hong Kong University of Science and Technology Hong Kong The Individualized Interdisciplinary Program The Robotics Institute Hong Kong The Department of Mathematics Hong Kong The Department of Computer Science and Engineering The Department of Electronic and Computer Engineering Hong Kong

We present a portable multiscopic camera system with a dedicated model for novel view and time synthesis in dynamic scenes. Our goal is to render high-quality images for a dynamic scene from any viewpoint at any time using our portable multiscopic camera. To achieve such novel view and time synthesis, we develop a physical multiscopic camera equipped with five cameras to train a neural radiance field (NeRF) in both time and spatial domains for dynamic scenes. Our model maps a 6D coordinate (3D spatial position, 1D temporal coordinate, and 2D viewing direction) to view-dependent and time-varying emitted radiance and volume density. Volume rendering is applied to render a photo-realistic image at a specified camera pose and time. To improve the robustness of our physical camera, we propose a camera parameter optimization module and a temporal frame interpolation module to promote information propagation across time. We conduct experiments on both real-world and synthetic datasets to evaluate our system, and the results show that our approach outperforms alternative solutions qualitatively and quantitatively. Our code and dataset are available at https://***/. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

Multi-legged matter transport: A framework for locomotion on noisy landscapes

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arXiv 2023年

作者： Chong, Baxi He, Juntao Soto, Daniel Wang, Tianyu Irvine, Daniel Blekherman, Grigoriy Goldman, Daniel I. Interdisciplinary Graduate Program in Quantitative Biosciences Georgia Institute of Technology North Avenue AtlantaGA30332 United States School of Physics Georgia Institute of Technology 837 State St NW AtlantaGA30332 United States Institute for Robotics and Intelligent Machines Georgia Institute of Technology 801 Atlantic Dr NW AtlantaGA30332 United States School of Mathematics Georgia Institute of Technology 686 Cherry St NW AtlantaGA30332 United States

While the transport of matter by wheeled vehicles or legged robots can be guaranteed in engineered landscapes like roads or rails, locomotion prediction in complex environments like collapsed buildings or crop fields remains challenging. Inspired by principles of information transmission which allow signals to be reliably transmitted over noisy channels, we develop a "matter transport" framework demonstrating that non-inertial locomotion can be provably generated over "noisy" rugose landscapes (heterogeneities on the scale of locomotor dimensions). Experiments confirm that sufficient spatial redundancy in the form of serially-connected legged robots leads to reliable transport on such terrain without requiring sensing and control. Further analogies from communication theory coupled to advances in gaits (coding) and sensor-based feedback control (error detection/correction) can lead to agile locomotion in complex terradynamic regimes. © 2023, CC BY.

关键词： Information theory

M2FN: Multi-step modality fusion for advertisement image assessment

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arXiv 2021年

作者： Park, Kyung-Wha Ha, Jung-Woo Lee, JungHoon Kwon, Sunyoung Kim, Kyung-Min Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Korea Republic of NAVER AI LAB NAVER CLOVA Statistics and Actuarial Science Soongsil University Korea Republic of School of Biomedical Convergence Engineering Pusan National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Surromind Robotics

Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in ad images. We, therefore, investigated the influence of these attributes on ad image preferences. First, we analyzed large-scale real-world ad log data and, based on our findings, proposed a novel multi-step modality fusion network (M2FN) that determines advertising images likely to appeal to user preferences. Our method utilizes auxiliary attributes through multiple steps in the network, which include conditional batch normalization-based low-level fusion and attention-based high-level fusion. We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world ad dataset with rich auxiliary attributes. © 2021, CC BY-NC-SA.

关键词： Marketing

Which ads to show? Advertisement image assessment with auxiliary information via multi-step modality fusion

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arXiv 2019年

作者： Park, Kyung-Wha Lee, JungHoon Kwon, Sunyoung Ha, Jung-Woo Kim, Kyung-Min Zhang, Byoung-Tak Interdisciplinary Program in Neuroscience Seoul National University Statistics and Actuarial Science Soongsil University Clova AI Research NAVER Corp Department of Computer Science and Engineering Seoul National University Surromind Robotics

Assessing aesthetic preference is a fundamental task related to human cognition. It can also contribute to various practical applications such as image creation for online advertisements. Despite crucial influences of image quality, auxiliary information of ad images such as tags and target subjects can also determine image preference. Existing studies mainly focus on images and thus are less useful for advertisement scenarios where rich auxiliary data are available. Here we propose a modality fusion-based neural network that evaluates the aesthetic preference of images with auxiliary information. Our method fully utilizes auxiliary data by introducing multi-step modality fusion using both conditional batch normalization-based low-level and attention-based high-level fusion mechanisms, inspired by the findings from statistical analyses on real advertisement data. Our approach achieved state-of-the-art performance on the AVA dataset, a widely used dataset for aesthetic assessment. Besides, the proposed method is evaluated on large-scale real-world advertisement image data with rich auxiliary attributes, providing promising preference prediction results. Through extensive experiments, we investigate how image and auxiliary information together influence click-through rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Image fusion