The goal of this paper is to improve our previous Dynamic Obstacle Mapping (DOMap) system by means of improving the perception stage. The new system must deal with robots and people as dynamic obstacles using LIght Detection And Range (LIDAR) sensor in order to collect the surrounding information. Although robot movement can be easily tracked by an Extended Kalman Filter (EKF), people’s movement is more unpredictable and it might not be correctly linearized by an EKF. Therefore, to deal with a better estimation of both types of dynamic objects in the local map it is recommended to improve our previous work. The DOMap has been extended in three key points: first the LIDAR reflectivity remission is used to make more robust the matching in the optical flow of the detection stage, secondly static and a dynamic occlusion detectors have been proposed, and finally a tracking stage based on Particle Filter (PF) has been used to deal with robots and people as dynamic obstacles. Therefore, our new improved-DOMap (iDOMap) provides maps with information about occupancy and velocities of the surrounding dynamic obstacles (robots, people, etc.) in a more robust way and they are available to improve the following planning stage.

中文翻译：

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改进的动态障碍物映射（iDOMap）

本文的目的是通过改善感知阶段来改进我们以前的动态障碍映射（DOMap）系统。为了收集周围的信息，新系统必须使用光检测和测距（LIDAR）传感器将机器人和人员视为动态障碍物。尽管可以通过扩展卡尔曼滤波器（EKF）轻松跟踪机器人的运动，但是人们的运动更加不可预测，并且可能无法通过EKF正确地线性化。因此，为了更好地估计本地地图中两种类型的动态对象，建议改进我们以前的工作。DOMap已在三个关键点上进行了扩展：首先使用LIDAR反射率反射使检测级的光流匹配更加鲁棒，其次提出了静态和动态遮挡检测器，最后，基于粒子过滤器（PF）的跟踪阶段已用于将机器人和人员视为动态障碍。因此，我们新的改进型DOMap（iDOMap）以更健壮的方式为地图提供了有关周围动态障碍物（机器人，人等）的占用和速度的信息，可用于改进以下规划阶段。