
 
 
 
 
 
 Autorlar : (1) Jiacui Huang, Senior, IEEE; (2) Hongtao Zhang, Senior, IEEE; 3) Mingbo Zhao, Senior, IEEE; Bilmiz o‘z, o‘z o‘z, o‘z o‘z. Authors: (1) Jiacui Huang, Senior, IEEE; (2) Hongtao Zhang, Senior, IEEE; 3) Mingbo Zhao, Senior, IEEE; Bilmiz o‘z, o‘z o‘z, o‘z o‘z. Solida tabellar Abstract və I. Introduction II. Sa'd işidir III. Metoda IV. Eksplorasi V. Konklusiyalar, bilan, referanslar VI. lisingiz —Vision-and-Language Navigation (VLN) is a challenging task that requires a robot to navigate in photo-realistic environments with human natural language promptings. Recent studies aim to handle this task by constructing the semantic spatial map representation of the environment, and then leveraging the strong ability of reasoning in large language models for generalizing code for guiding the robot navigation. However, these methods face limitations in instance-level and attribute-level navigation tasks as they cannot distinguish different instances of the same object. To address this challenge, we propose a new method, namely, Instance-aware Visual Language Map (IVLMap), to empower the robot with instance-level and attribute-level semantic mapping, where it is autonomously constructed by fusing the RGBD video data collected from the robot agent with special-designed natural language map indexing in the bird’s-in-eye view. Such indexing is instancelevel and attribute-level. In particular, when integrated with a large language model, IVLMap demonstrates the capability to i) transform natural language into navigation targets with instance and attribute information, enabling precise localization, and ii) accomplish zero-shot end-to-end navigation tasks based on natural language commands. Extensive navigation experiments are conducted. Simulation results illustrate that our method can achieve an average improvement of 14.4% in navigation accuracy. Code and demo are released at https://ivlmap.github.io/. Abstract Abstraksiya I. Introduction Robotlar o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z Uga, VLN o‘zingizga qaytarib, o‘zingizga qaytarib, o‘zingizga qaytarib, o‘zingizga qaytarib, o‘zingizga qaytarib, o‘zingizga qaytarib, o‘zigarib, o‘zigarib, o‘zigarib, o‘zigarib, o‘zigarib, o‘zigarib, o‘zigarib, o‘zigarib, o‘zigarib, o‘zigarib. Biz o‘z bilan o‘zadi, o‘zadi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi, o‘zidi. Bu doktrinada, biz RGBD video data yo‘ladi, o‘z bird’s-in-eye view indexing, o‘z bird’s-in-eye indexing, o‘z bird’s-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird-in-eye indexing, o‘z bird 
 
 
 
 
 
 a novel instance-level and attribute-level map in bird’sin-eye review is developed for better comprehend the environment and robot navigation planning, where the instances in map are obtained by involving region matching and label scoring to achieve the category label for each SAM [10]-segmented mask, while the color attribute labels for each mask are also obtained by using the similar approach. 
 
 leveraging IVLMap, we propose a two-step method for locating landmarks. This involves an initial coarsegrained localization of the corresponding mask, followed by a fine-grained localization and navigation within the mask’s designated region. 
 
 we established an interactive data collection platform, achieving real-time controllable data acquisition. This not only reduced data volume but also enhanced reconstruction efficiency. Furthermore, we conducted experiments in a real-world environment, providing valuable insights for the practical deployment of this method. Biz o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ II. Sa'd işidir Konvolutionary Neural Networks (CNNs) [11] and dense Simultaneous Localization and Mapping (SLAM) [12] integration, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping, simantic mapping Semantic Mapping. Inovative research [16] sizga human instance segmentation sizga distinctive detection phases. Real-time solutions [17] sizga Space Attention-Guided Mask (SAG-Mask) filani. Meta AI's Segment Anything Model (SAM) [10] sizga segmentation, qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmizga qilmidir. Instance Segmentation VIM-Net o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ Vision-and-Language Navigation(VLN) Large Language Models (LLMs) o‘z VLN’da qalin rol oynayadi, NavGPT’nin argomentiga qilmadi [23]. LM-Nav GPT-3 o‘z qilmadi [4]. VELMA o‘z LLMs o‘z street level navigation, o‘z AI o‘z qilmadi [24]. Studiyalar o‘z Vemprala et al.’s integration of ChatGPT into robotics applications and PaLM-E’s multimodal reasoning with visual and textual inputs [25], [26] influence our project, where we achieved accurate navigation using LLMs guided by natural language. LLM used in VLN. 
 
 Bu dokimi CC by 4.0 Deed (Attribution 4.0 International) lisenzonda arxiv qaytaradi. Bu dokimi CC by 4.0 Deed (Attribution 4.0 International) lisenzonda arxiv qaytaradi. O‘z arxiv

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