LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF

Zhu, Zhenxin; Chen, Yuantao; Wu, Zirui; Hou, Chenping; Shi, Yongliang; Li, Chuxuan; Li, Pengfei; Zhao, Hao; Zhou, Guyue

doi:10.1109/icra48891.2023.10161570

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…In this phase, ANM aims to achieve key technological breakthroughs and advancements through massive amounts of standard datasets [2], [52]. The community is experiencing thorough refinement and modularization of traditional methods, maturation of deep learning approaches [2], [52], and breakthrough of novel frameworks (e.g., NeRF-based localization [53] and mapping [54]). During this stage, datasets must be high-quality, all-rounded, and possess significant scale for training, testing, and improvement [52], [55].…”

Section: B Datasets For Anmmentioning

confidence: 99%

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

Liu,

Fu,

Qin

et al. 2024

IEEE Trans. Intell. Veh.

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The development of intelligent Vehicles (IVs) requires extensive standard datasets for training, benchmarking, and improvement. Autonomous Navigation and Mapping (ANM), as a critical technology for IVs, imposes exceptionally high demands on dataset construction. This is significant in its requirements for comprehensive sensor calibration, precise time synchronization, and accurate generation of ground truth. Besides, the whole construction workflow also demands intricate knowledge and sophisticated practices, necessitating lengthy learning curves for researchers to attain proficiency. The above challenges have led to a slow production of qualified datasets, directly constraining the advancement of ANM. However, so far, an investigation focused on a mature construction methodology of ANM dataset is still missing. This paper strives to fill the gap. Specifically, based on our systematic reviews and extensive practices, for the first time, a full-stack construction methodology of ANM dataset is proposed, including modules of platform construction, sensor calibration, time synchronization, ground truth generation, synthetic data production, and benchmark criteria, with detailed techniques and methodological routes provided in each step. Several long-standing issues are resolved within the methodology. Importantly, we introduce versatile calibration and synchronization frameworks that attain up to us-level and mmlevel precision. Besides, we propose a full-scenario ground truth system that can generate scene-map and trajectory at cm-level accuracy. To verify the effectiveness of our methodology, we design a high-quality dataset and benchmark multiple state-ofthe-art algorithms on it. The successful workflow demonstrates that our methodology can significantly reduce the research threshold and help individuals and institutions to construct datasets in a standardized way.

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Section: B Datasets For Anmmentioning

confidence: 99%

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

Liu,

Fu,

Qin

et al. 2024

IEEE Trans. Intell. Veh.

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“…Therefore, NeRF models have found diverse applications across fields such as photo-realistic 3D editing (39), medical 3D image reconstruction (40), and neural scene representations for world mapping (41). NeRFs have also shown potential in areas like the industry and robotics domain (42), autonomous navigation (43), and augmented and virtual reality (44), where they carry the potential to lead to more efficient techniques for capturing and generating human 3D avatars and objects in the metaverse (45,46).…”

Section: Neural Radiance Fieldsmentioning

confidence: 99%

Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry

Tortora

2024

Front. Psychiatry

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The advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing paradigm through their ability to generate seemingly new realistic data and analyse and integrate a vast amount of unstructured content from different data formats. This potential extends beyond reshaping conventional practices, like risk assessment, diagnostic support, and treatment and rehabilitation plans, to creating new opportunities in previously underexplored areas, such as training and education. This paper examines the transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice. First, it introduces generative AI and its prevalent models. Following this, it reviews the current applications of discriminative AI in forensic psychiatry. Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. Finally, it provides a comprehensive overview of ethical and legal issues associated with deploying generative AI models, focusing on their impact on individuals as well as their broader societal implications. In conclusion, this paper aims to contribute to the ongoing discourse concerning the dynamic challenges of generative AI applications in forensic contexts, highlighting potential opportunities, risks, and challenges. It advocates for interdisciplinary collaboration and emphasises the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made.

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An Evaluation of Quality Stability in NeRF Object Modeling

Luo,

Flaherty,

Yang

et al. 2024

2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)

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LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF

Cited by 9 publications

References 21 publications

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry

An Evaluation of Quality Stability in NeRF Object Modeling

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