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

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL ISPRS Journal of Photogrammetry and Remote Sensing Pub Date : 2024-08-24 DOI:10.1016/j.isprsjprs.2024.08.007
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引用次数: 0

摘要

在复杂的地下空间进行自主探索、无人驾驶运输和紧急救援行动时,准确而稳健的同步定位和绘图(SLAM)是必不可少的。然而,地下空间光照差、纹理弱、灰尘大,这些苛刻的条件给 SLAM 带来了巨大挑战。为了解决这个问题,我们提出了一种基于图的自适应加权融合 SLAM(AWF-SLAM),用于自主机器人在复杂的地下空间实现精确、稳健的 SLAM。首先,我们提出了一种对比度受限的自适应直方图均衡(CLAHE),它结合了色调、饱和度和值(HSV)空间的自适应伽玛校正与加权分布(AGCWD),以增强地下空间视觉图像的亮度和对比度。然后,利用基于 Mahalanobis 距离的一致性检查来评估每个传感器的性能,从而为特定条件选择最佳配置。随后,我们阐述了一个自适应加权函数模型,该模型利用了点云匹配的残差和图像匹配的内点率。该模型可动态融合来自光探测与测距(LiDAR)、惯性测量单元(IMU)和摄像头的数据,从而提高融合过程的灵活性。最后,利用滑动窗口方法,在因子图优化范围内对多个原始特征进行自适应融合。基于最先进方法提供的参考轨迹和重构,使用自行设计的移动机器人在地下停车场、挖掘的地铁隧道和复杂的地下煤矿空间进行了广泛的实验,以检验 AWF-SLAM 的性能。令人满意的是,轨迹平移的均方根误差(RMSE)仅为 0.17 m,AWF-SLAM 重建的点云图与参考点云图之间的平均相对鲁棒性距离低于 0.09 m。
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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.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
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.
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