An Expeditious Spatial Mean Radiant Temperature Mapping Framework using Visual SLAM and Semantic Segmentation

• URL: http://arxiv.org/abs/2410.09443v1
• Date: Sat, 12 Oct 2024 08:50:35 GMT
• Title: An Expeditious Spatial Mean Radiant Temperature Mapping Framework using Visual SLAM and Semantic Segmentation
• Authors: Wei Liang, Yiting Zhang, Ji Zhang, Erica Cochran Hameen,
• Abstract summary: Mean radiant temperature (MRT) is very challenging to measure. Most common measurement methodologies are time-consuming and not user-friendly. This paper proposes a novel MRT measurement framework that uses visual simultaneous localization and mapping (SLAM) and semantic segmentation techniques.
• Score: 7.1869288530375135
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Ensuring thermal comfort is essential for the well-being and productivity of individuals in built environments. Of the various thermal comfort indicators, the mean radiant temperature (MRT) is very challenging to measure. Most common measurement methodologies are time-consuming and not user-friendly. To address this issue, this paper proposes a novel MRT measurement framework that uses visual simultaneous localization and mapping (SLAM) and semantic segmentation techniques. The proposed approach follows the rule of thumb of the traditional MRT calculation method using surface temperature and view factors. However, it employs visual SLAM and creates a 3D thermal point cloud with enriched surface temperature information. The framework then implements Grounded SAM, a new object detection and segmentation tool to extract features with distinct temperature profiles on building surfaces. The detailed segmentation of thermal features not only reduces potential errors in the calculation of the MRT but also provides an efficient reconstruction of the spatial MRT distribution in the indoor environment. We also validate the calculation results with the reference measurement methodology. This data-driven framework offers faster and more efficient MRT measurements and spatial mapping than conventional methods. It can enable the direct engagement of researchers and practitioners in MRT measurements and contribute to research on thermal comfort and radiant cooling and heating systems.

Related papers

• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas [1.124958340749622]
  This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. A subset of the thermal image dataset was used to train state-of-the-art deep learning models to segment various urban features.
  arXiv  Detail & Related papers  (2023-10-06T13:41:39Z)
• Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators [0.0]
  This paper presents a data-driven model that captures the local temperature evolution during the additive manufacturing process. It is tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process. The results demonstrate that the model achieves high fidelity as measured by $R2$ and maintains generalizability to geometries that were not included in the training process.
  arXiv  Detail & Related papers  (2023-07-04T16:17:59Z)
• A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction [66.21060114843202]
  We propose a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure.
  arXiv  Detail & Related papers  (2023-05-30T13:58:50Z)
• Precise Facial Landmark Detection by Reference Heatmap Transformer [52.417964103227696]
  We propose a novel Reference Heatmap Transformer (RHT) for more precise facial landmark detection. The experimental results from challenging benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art methods in the literature.
  arXiv  Detail & Related papers  (2023-03-14T12:26:48Z)
• Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning [2.449329947677678]
  The aim of this research is to explore the use of data-driven models in measuring temperature distributions. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN) The proposed method is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.
  arXiv  Detail & Related papers  (2022-12-15T13:46:15Z)
• Does Thermal Really Always Matter for RGB-T Salient Object Detection? [153.17156598262656]
  This paper proposes a network named TNet to solve the RGB-T salient object detection (SOD) task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality.
  arXiv  Detail & Related papers  (2022-10-09T13:50:12Z)
• Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning [64.92447072894055]
  Infrared (IR) cameras are robust under adverse illumination and lighting conditions. We propose an algorithm meta-learning framework to improve existing UDA methods. We produce a state-of-the-art thermal detector for the KAIST and DSIAC datasets.
  arXiv  Detail & Related papers  (2021-10-07T02:28:18Z)
• Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks [38.35547654117047]
  We propose the first complete thermal-inertial SLAM system which combines neural abstraction in the SLAM front end with robust pose graph optimization in the SLAM back end. Our key strategies to successfully model this encoding from thermal imagery are the usage of normalized 14-bit radiometric data, the incorporation of hallucinated visual (RGB) features, and the inclusion of feature selection to estimate the MDN parameters.
  arXiv  Detail & Related papers  (2021-04-15T01:39:15Z)
• A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation [50.261472059743845]
  Action Units (AUs) are geometrically-based atomic facial muscle movements. We propose a novel AU modelling problem that consists of jointly estimating their localisation and intensity. A Heatmap models whether an AU occurs or not at a given spatial location.
  arXiv  Detail & Related papers  (2020-04-14T16:51:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.