College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China.
School of Artificial Intelligence, Jilin University, Changchun, People's Republic of China.
PLoS One. 2021 Dec 8;16(12):e0261053. doi: 10.1371/journal.pone.0261053. eCollection 2021.
Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.
准确可靠的状态估计和建图是大多数自动驾驶系统的基础。近年来，研究人员专注于通过几何特征匹配进行位姿估计。然而，文献中的大多数工作都假设是静态场景。此外，基于几何特征的配准容易受到动态物体的干扰，导致精度下降。随着深度语义分割网络的发展，我们可以方便地从点云中获取语义信息，除了几何信息。语义特征可以作为几何特征的补充，从而提高里程计和闭环检测的性能。在更现实的环境中，语义信息可以滤除数据中的动态物体，如行人和车辆，这会导致生成的地图中的信息冗余和基于地图的定位失败。在本文中，我们提出了一种名为 LiDAR 惯性里程计（LIO）与闭环结合语义信息（LIO-CSI）的方法，该方法集成了语义信息，以方便前端处理和闭环检测。首先，我们对 Sparse Point-Voxel Neural Architecture Search (SPVNAS) 网络提供的语义标签进行局部优化。优化后的语义信息通过平滑和建图（LIO-SAM）被合并到紧耦合的激光雷达惯性里程计（LiDAR）前端处理中，这允许我们过滤动态物体并提高点云注册的准确性。然后，我们提出了一种语义辅助扫描上下文方法，以提高闭环检测的准确性和鲁棒性。实验在广泛使用的 KITTI 数据集和吉林大学（JLU）校园的自采集数据集上进行。实验结果表明，我们的方法优于纯几何方法，特别是在动态场景中，具有良好的泛化能力。
