Semi-Automatic Town-Scale 3D Mapping using Building Information from Publicly Available Maps
Semi-Automatic Town-Scale 3D Mapping using Building Information from Publicly Available Maps
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DOI：
10.1109/access.2022.3150387
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Shun Niijima;Ryusuke Umeyama;Y. Sasaki;H. Mizoguchi
中科院分区：
文献类型：
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作者：
Shun Niijima;Ryusuke Umeyama;Y. Sasaki;H. Mizoguchi
三维（3D）地图用于自定位估计和路径规划的机器人在城市中的自主导航。本文提出了一个框架，用于生成全球一致的三维地图的城市地区。我们的方法通过在公开可用的地图上执行与建筑物信息的3D对齐来校正一致的3D地图。该框架自动为对齐找到合适的锚姿势，并根据对齐获得的约束优化姿势图。然而，存在由于测量误差以及公开可用地图中的误差而难以自动化3D对准的情况。为了最大限度地降低运营成本，建议的框架采用了用户界面（UI），允许用户检查3D地图对齐的结果，并进行简单的校正。通过在日本城市地区进行3D测绘实验来评估该框架，并在大约15公里的距离上进行3D测绘。实验结果表明，该框架能够以较高的概率自动选择锚位姿，生成大比例尺的3D城市地图，用户平均每公里手动操作约5次。通过将其与基于商业移动的标测系统（MMS）的精确3D标测图的手动校正参考轨迹进行比较，评估3D标测的准确性。3D地图的平均绝对位置误差为5.5，与其他开源软件（OSS）SLAM方法生成的地图相比，这是最低的误差。
The three-dimensional (3D) maps are used for self-positioning estimation and path planning for the autonomous navigation of robots in cities. This paper presents a framework for generating globally consistent 3D maps of urban areas. Our approach corrects consistent 3D maps by performing a 3D alignment with building information on publicly available maps. The framework automatically finds an appropriate anchor pose for the alignment and optimizes the pose graph according to the constraints obtained by the alignment. However, there are situations where it is difficult to automate 3D alignment because of measurement errors as well as errors in publicly available maps. To minimize operational costs, the proposed framework incorporates a user interface (UI) that allows users to check the results of 3D map alignment and make simple corrections. The framework was evaluated by conducting 3D mapping experiments in an urban area in Japan, and 3D mapping was performed over a distance of approximately 15 kilometers. The experimental results showed that the framework could automatically select anchor poses with high probability and generate a large-scale 3D city map with an average of approximately five manual operations per km by the user. The accuracy of the 3D mapping was evaluated by comparing it with a manually corrected reference trajectory based on an accurate 3D map from a commercial mobile mapping system (MMS). The 3D maps had an average absolute position error of 5.5, which is the lowest error compared to the maps generated by other open source software (OSS) SLAM methods.