.png){kind=icon}
返回
Improved Dynamic Obstacle Mapping (iDOMap)
.png){kind=icon}
OA
AI
DOI：10.3390/s20195520.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Dynamic Obstacles Mapping (DOMap)
Particle Filter
optical flow
dynamic occlusion detector
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
分享收藏Coaches’ implicit associations between size and giftedness: implications for the relative age effect
PREAI
分享收藏
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving同时定位和映射: 对自动驾驶当前趋势的调查OAAI
OAAI分享收藏OctoMap: an efficient probabilistic 3D mapping framework based on octreesOctoMap: 一种基于八叉树的高效概率三维映射框架.png)
AUTONOMOUS ROBOTSOAAI
AUTONOMOUS ROBOTSIF4.3
OAAI分享收藏
查看更多内容