Simultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.

基于刚性场景假设的同步定位和建图系统无法在具有大量移动物体的复杂环境中实现可靠的定位和建图。针对这一问题，本文提出了一种基于语义对象识别技术的新型动态多目标激光雷达里程计（MLO）系统。该系统能够可靠地定位机器人和语义对象，并在复杂的动态场景中生成长期静态地图。对于自我运动估计，所提出的系统提取考虑语义和几何一致性约束的环境特征。然后，过滤后的特征可以对语义可移动和未知的动态对象具有鲁棒性。此外，我们提出了一种新的最小二乘估计器，它使用几何对象点和语义框平面来稳健且精确地实现多对象跟踪（SGF-MOT）任务。在映射模块中，我们使用绝对轨迹跟踪列表实现动态语义对象检测。通过使用静态语义对象和环境特征，系统消除了累积的定位误差并生成纯静态地图。在公共 KITTI 数据集上的实验表明，与现有技术相比，所提出的 MLO 系统在复杂场景中提供了更准确和鲁棒的目标跟踪性能以及更好的实时定位精度。