Abstract With the rapid development of computer vision, vision-based simultaneous localization and mapping (vSLAM) plays an increasingly important role in the field of unmanned driving. However, traditional SLAM methods based on a monocular camera only perform well in simple indoor environments or urban environments with obvious structural features. In off-road environments, the situation that SLAM encounters could be complicated by problems such as direct sunlight, leaf occlusion, rough roads, sensor failure, sparsity of stably trackable texture. Traditional methods are highly susceptible to these factors, which lead to compromised stability and reliability. To counter such problems, we propose a panoramic vision SLAM method based on multi-camera collaboration, aiming at utilizing the characters of panoramic vision and stereo perception to improve the localization precision in off-road environments. At the same time, the independence and information sharing of each camera in multi-camera system can improve its ability to resist bumps, illumination, occlusion and sparse texture in an off-road environment, and enable our method to recover the metric scale. These characters ensure unmanned ground vehicles (UGVs) to locate and navigate safely and reliably in complex off-road environments.

中文翻译：

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用于越野导航的多相机视觉SLAM

摘要 随着计算机视觉的快速发展，基于视觉的同步定位与建图（vSLAM）在无人驾驶领域发挥着越来越重要的作用。然而，传统的基于单目相机的SLAM方法只能在简单的室内环境或具有明显结构特征的城市环境中表现良好。在越野环境中，SLAM遇到的情况可能会因阳光直射、树叶遮挡、崎岖道路、传感器故障、稳定可跟踪纹理稀疏等问题而变得复杂。传统方法非常容易受到这些因素的影响，从而导致稳定性和可靠性受到影响。针对这些问题，我们提出了一种基于多相机协作的全景视觉SLAM方法，旨在利用全景视觉和立体感知的特点，提高越野环境下的定位精度。同时，多相机系统中每个相机的独立性和信息共享可以提高其在越野环境中抵抗颠簸、光照、遮挡和稀疏纹理的能力，并使我们的方法能够恢复度量尺度。这些特性确保无人地面车辆 (UGV) 在复杂的越野环境中安全可靠地定位和导航。