{"title":"在复杂地下空间使用多模态数据的基于图的自适应加权融合 SLAM","authors":"","doi":"10.1016/j.isprsjprs.2024.08.007","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.</p></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces\",\"authors\":\"\",\"doi\":\"10.1016/j.isprsjprs.2024.08.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.</p></div>\",\"PeriodicalId\":50269,\"journal\":{\"name\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2024-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0924271624003198\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS Journal of Photogrammetry and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924271624003198","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces
Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.