The SLAM system based on static scene assumptions introduce significant estimation errors when there are many moving objects in the field of view. Tracking and maintaining semantic objects is beneficial for scene understanding and providing rich decision-making information for planning and control modules. This paper introduces MLO, a multi-object lidar odometry that tracks ego-motion and semantic objects using only a lidar sensor. To achieve accurate and robust tracking of multiple objects, we propose a least-squares estimator that fuses 3D bounding boxes and geometric point clouds for object state updating. By analyzing the object motion states in the tracking list, the mapping module uses static objects and environmental features to eliminate accumulated errors. At the same time, it provides continuous object trajectories in map coordinate. Our method is qualitatively and quantitatively evaluated in different scenarios under the public KITTI dataset. The experiment results show that the ego localization accuracy of MLO is better than the state-of-the-art systems in highly dynamic, unstructured, and unknown semantic scenes. Meanwhile, the multi-target tracking method with semantic-geometry fusion also has apparent advantages in tracking accuracy and consistency compared with the filtering-based method.
翻译:以静态场景假设为基础的 SLAM 系统在观察领域有许多移动物体时引入了重大估计错误。 跟踪和维护语义对象有助于了解现场,并为规划和控制模块提供丰富的决策信息。 本文介绍了 MLO, 这是一种多对象的利达尔 odoter 方法, 仅使用 lidar 传感器跟踪自动和语义对象。 为了准确和有力地跟踪多个对象, 我们提议了一个最小方位估计器, 它将三维捆绑框和几何点云连接起来, 用于更新对象状态。 通过分析跟踪列表中的物体运动状态, 绘图模块使用静态对象和环境特征来消除累积错误。 同时, 它提供了地图协调中的连续对象轨迹。 我们的方法是在公共 KITTI 数据集下对不同情景进行定性和定量评估。 实验结果显示, MLO 的自我本地化精确度比在高度动态、 不结构化和未知的语义状态系统中的状态系统要好。 同时, 多目标跟踪方法使用静态对象- 过滤器的精确性也具有明显的优势。