Highly accurate 2D maps can supply basic geospatial information for efficient and accurate indoor building modeling. However, problematic scenarios, which are characterized by few features, similar components and large scales, seriously influence data association and cumulative error elimination, and thus degrade simultaneous localization and mapping (SLAM)-based mapping quality. In this paper, a cross-correction LiDAR SLAM method is proposed for constructing high-accuracy 2D maps of problematic scenarios. The method comprises two models. The first model, namely, pose correction for rough mapping (PCRM), increases the data association capacity and generates a rough map with cumulative errors. In the PCRM model, a rough mapping module is developed against the scenario with few features for accurate data association. This module improves the robustness of the data association by using the initial poses from the local pose correction module, especially in similar-component scenarios. The other is a map correction for pose optimization (MCPO) model, which enhances cumulative error elimination capacity. Here, a block-based local map correction module is proposed that takes both map and pose into consideration to construct accurate constraints. The constraints are then added to the global pose optimization module to significantly reduce the cumulative error of the rough map and thus construct a high-accuracy 2D map. The results demonstrate the superiority of our method over 5 other state-of-the-art methods in problematic scenarios. The overall performance of our method in these two scenarios is approximately 1 cm and 0.2% in terms of the absolute and relative map errors, respectively. Moreover, the modeling results demonstrate that our method can be applied to the efficient and accurate indoor modeling.

高精度2D地图可以提供基本的地理空间信息，以进行高效，准确的室内建筑建模。但是，具有少量特征，相似组件和大规模特征的问题场景会严重影响数据关联和累积错误消除，从而降低基于同时定位和映射（SLAM）的映射质量。本文提出了一种交叉校正LiDAR SLAM方法，用于构造问题场景的高精度二维地图。该方法包括两个模型。第一种模型，即用于粗映射的姿势校正（PCRM），增加了数据关联能力并生成具有累积误差的粗映射。在PCRM模型中，针对该场景开发了一个粗略的映射模块，该模块具有很少的功能以实现准确的数据关联。该模块通过使用本地姿态校正模块中的初始姿态来提高数据关联的鲁棒性，尤其是在类似组件的情况下。另一个是姿势优化的地图校正（MCPO）模型，可增强累积错误消除能力。在此，提出了一种基于块的局部地图校正模块，该模块同时考虑了地图和姿态，以构造准确的约束。然后将约束添加到全局姿势优化模块，以显着减少粗略图的累积误差，从而构造高精度2D图。结果表明，在有问题的情况下，我们的方法优于其他5种最先进的方法。在这两种情况下，我们的方法的整体性能约为1 cm和0。就绝对和相对地图误差而言，分别为2％。而且，建模结果表明我们的方法可以应用于高效，准确的室内建模。