Frosi Matteo, Gobbi Veronica, Matteucci Matteo
Dipartimento di Elettronica, Informazione e Bioingegneria of Politecnico di Milano, Milan, Italy.
Front Robot AI. 2023 Mar 29;10:1064934. doi: 10.3389/frobt.2023.1064934. eCollection 2023.
In the last decades, Simultaneous Localization and Mapping (SLAM) proved to be a fundamental topic in the field of robotics, due to the many applications, ranging from autonomous driving to 3D reconstruction. Many systems have been proposed in literature exploiting a heterogeneous variety of sensors. State-of-the-art methods build their own map from scratch, using only data coming from the equipment of the robot, and not exploiting possible reconstructions of the environment. Moreover, temporary loss of data proves to be a challenge for SLAM systems, as it demands efficient re-localization to continue the localization process. In this paper, we present a SLAM system that exploits additional information coming from mapping services like OpenStreetMaps, hence the name OSM-SLAM, to face these issues. We extend an existing LiDAR-based Graph SLAM system, ART-SLAM, making it able to integrate the 2D geometry of buildings in the trajectory estimation process, by matching a prior OpenStreetMaps map with a single LiDAR scan. Each estimated pose of the robot is then associated with all buildings surrounding it. This association allows to improve localization accuracy, but also to adjust possible mistakes in the prior map. The pose estimates coming from SLAM are then jointly optimized with the constraints associated with the various OSM buildings, which can assume one of the following types: Buildings are always fixed (Prior SLAM); buildings surrounding a robot are movable in chunks, for every scan (Rigid SLAM); and every single building is free to move independently from the others (Non-rigid SLAM). Lastly, OSM maps can also be used to re-localize the robot when sensor data is lost. We compare the accuracy of the proposed system with existing methods for LiDAR-based SLAM, including the baseline, also providing a visual inspection of the results. The comparison is made by evaluating the estimated trajectory displacement using the KITTI odometry dataset. Moreover, the experimental campaign, along with an ablation study on the re-localization capabilities of the proposed system and its accuracy in loop detection-denied scenarios, allow a discussion about how the quality of prior maps influences the SLAM procedure, which may lead to worse estimates than the baseline.
在过去几十年中，同时定位与地图构建（SLAM）已被证明是机器人技术领域的一个基础课题，这得益于其众多的应用，涵盖从自动驾驶到三维重建等领域。文献中已经提出了许多利用各种不同类型传感器的系统。当前的先进方法仅使用来自机器人设备的数据从头构建自己的地图，而不利用环境的可能重建信息。此外，数据的临时丢失对SLAM系统来说是一个挑战，因为这需要高效的重新定位以继续定位过程。在本文中，我们提出了一种SLAM系统，该系统利用来自诸如OpenStreetMaps等地图服务的额外信息，因此名为OSM-SLAM，以应对这些问题。我们扩展了现有的基于激光雷达的图优化SLAM系统ART-SLAM，使其能够通过将预先存在的OpenStreetMaps地图与单次激光雷达扫描进行匹配，在轨迹估计过程中整合建筑物的二维几何信息。然后，机器人的每个估计位姿都与周围的所有建筑物相关联。这种关联不仅可以提高定位精度，还可以校正预先地图中可能存在的错误。来自SLAM的位姿估计随后与与各种OSM建筑物相关联的约束条件一起进行联合优化，这些约束条件可以采用以下类型之一：建筑物始终固定（先验SLAM）；围绕机器人的建筑物在每次扫描时可以成块移动（刚体SLAM）；并且每个单独的建筑物可以独立于其他建筑物自由移动（非刚体SLAM）。最后，当传感器数据丢失时，OSM地图还可用于重新定位机器人。我们将所提出系统的精度与现有的基于激光雷达的SLAM方法进行比较，包括基线方法，同时还对结果进行了可视化检查。通过使用KITTI里程计数据集评估估计轨迹的位移来进行比较。此外，实验活动以及对所提出系统的重新定位能力及其在闭环检测被拒绝场景中的精度的消融研究，使得我们能够讨论预先地图的质量如何影响SLAM过程，这可能导致比基线更差的估计结果。
