In this paper we present a system to enhance the performance of feature correspondence based alignment algorithms for laser scan data. We show how this system can be utilized as a new approach for evaluation of mapping algorithms. Assuming a certain a priori knowledge, our system augments the sensor data with hypotheses (‘Virtual Scans’) about ideal models of objects in the robot’s environment. These hypotheses are generated by analysis of the current aligned map estimated by an underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between data alignment and data analysis confirms, modifies, or discards the Virtual Scans in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach. By replacing the estimated ‘Virtual Scans’ with ground truth maps our system can provide a flexible way for evaluating different mapping algorithms in different settings.

中文翻译：

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使用虚拟扫描来改善映射和评估。

在本文中，我们提出了一种用于增强基于特征对应的激光扫描数据对齐算法性能的系统。我们展示了如何将此系统用作评估映射算法的新方法。假设您具有一定的先验知识，我们的系统将使用有关机器人环境中理想对象模型的假设（“虚拟扫描”）来扩充传感器数据。这些假设是通过对由基础迭代对齐算法估计的当前对齐图进行分析而生成的。增强的数据用于改善对齐过程。数据对齐和数据分析之间的反馈会在每次迭代中确认，修改或丢弃虚拟扫描。用模拟场景和救援机器人场景中的真实世界数据进行的实验表明了该方法的适用性和优势。