Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related Works
2.1. Vision-and-Language Navigation
2.2. Semantic Scene Understanding and Instance Segmentation
2.3. 3D Scene Reconstruction


Methodology
3.1. Data Collection
3.2. Open-set Semantic Information from Images
3.3. Creating the Open-set 3D Representation
3.4. Language-Guided Navigation


Experiments
4.1. Quantitative Evaluation
4.2. Qualitative Results


Conclusion and Future Work, Disclosure statement, and References Related Works
2.1. Vision-and-Language Navigation
2.2. Semantic Scene Understanding and Instance Segmentation
2.3. 3D Scene Reconstruction Related Works 2.1. Vision-and-Language Navigation 2.1. Vision-and-Language Navigation 2.2. Semantic Scene Understanding and Instance Segmentation 2.2. Semantic Scene Understanding and Instance Segmentation 2.3. 3D Scene Reconstruction 2.3. 3D Scene Reconstruction Methodology
3.1. Data Collection
3.2. Open-set Semantic Information from Images
3.3. Creating the Open-set 3D Representation
3.4. Language-Guided Navigation Methodology Methodology 3.1. Data Collection 3.1. Data Collection 3.2. Open-set Semantic Information from Images 3.2. Open-set Semantic Information from Images 3.3. Creating the Open-set 3D Representation 3.3. Creating the Open-set 3D Representation 3.4. Language-Guided Navigation 3.4. Language-Guided Navigation Experiments
4.1. Quantitative Evaluation
4.2. Qualitative Results Experiments Experiments 4.1. Quantitative Evaluation 4.1. Quantitative Evaluation 4.2. Qualitative Results 4.2. Qualitative Results Conclusion and Future Work, Disclosure statement, and References Conclusion and Future Work, Disclosure statement, and References Conclusion and Future Work, Disclosure statement, and References 3.1. Data Collection Creating the O3D-SIM begins by capturing a sequence of RGB-D images using a posed camera, with an estimate of the extrinsic and intrinsic parameters of the environment to be mapped. The pose information associated with each image is used to transform the point clouds to a world coordinate frame. For simulations, we use the groundtruth pose associated with each image, whereas we leverage RTAB-Map[30] with G2O optimization [31] in the real world to generate these poses. Authors:
(1) Laksh Nanwani, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(2) Kumaraditya Gupta, International Institute of Information Technology, Hyderabad, India;
(3) Aditya Mathur, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(4) Swayam Agrawal, International Institute of Information Technology, Hyderabad, India;
(5) A.H. Abdul Hafez, Hasan Kalyoncu University, Sahinbey, Gaziantep, Turkey;
(6) K. Madhava Krishna, International Institute of Information Technology, Hyderabad, India. Authors: Authors: (1) Laksh Nanwani, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work; (2) Kumaraditya Gupta, International Institute of Information Technology, Hyderabad, India; (3) Aditya Mathur, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work; (4) Swayam Agrawal, International Institute of Information Technology, Hyderabad, India; (5) A.H. Abdul Hafez, Hasan Kalyoncu University, Sahinbey, Gaziantep, Turkey; (6) K. Madhava Krishna, International Institute of Information Technology, Hyderabad, India. This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license. This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license. available on arxiv available on arxiv

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3D Mapping Initialization: Using RGB-D Images and Camera Parameters

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