Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.
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机译：变更检测是以有效的方式处理移动激光雷达数据的多个时期的重要工具，因为它允许通过专注于感兴趣的区域来应对其他耗时的操作。最先进的方法通常不处理不完全观察的情况，或者计算昂贵。我们提出了一种基于能够处理所述壳体的点云和体素组合的新方法，从而计算比可比方法昂贵。此外，除了通过传统方法识别的出现的和消失的物体之外，我们的方法能够识别特殊的变化，例如部分移动，完全移动和变形物体。我们的方法的性能是使用公共可用的Tum City Campus数据集进行评估，表现出88％的整体准确性。
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