Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment
复制标题
DOI：
10.1016/j.robot.2019.03.012
复制
发表时间：
2019-07-01
影响因子：
4.3
通讯作者：
Zou, Xudong
中科院分区：
文献类型：
--
作者：
Xiao, Linhui;Wang, Jinge;Zou, Xudong
在动态环境中工作时，传统的同时定位与地图构建（SLAM）框架由于动态物体的干扰而性能不佳。利用深度学习在物体检测方面的优势，提出了一种名为动态 - SLAM的语义同时定位与地图构建框架，以解决动态环境中的SLAM问题。首先，基于卷积神经网络，构建了一个结合先验知识的SSD物体检测器，以便在新的检测线程中从语义层面检测动态物体。然后，针对现有SSD物体检测网络召回率低的问题，提出了一种基于相邻帧速度不变性的漏检补偿算法，极大地提高了检测的召回率。最后，构建了一个基于特征的视觉SLAM系统，该系统在跟踪线程中通过选择性跟踪算法处理动态物体的特征点，以显著减少因错误匹配导致的位姿估计误差。与原始SSD网络相比，该系统的召回率从82.3%提高到了99.8%。多项实验表明，动态 - SLAM的定位精度高于现有最先进的系统。通过使用移动机器人，该系统在现实世界的动态环境中成功地进行了定位并构建了准确的环境地图。总之，我们的实验证明，与动态环境中现有的最先进的SLAM系统相比，动态 - SLAM在机器人定位和地图构建方面表现出更高的精度和更强的鲁棒性。（C）2019爱思唯尔有限公司。保留所有权利。
When working in dynamic environment, traditional SLAM framework performs poorly due to interference from dynamic objects. By taking advantages of deep learning in object detection, a semantic simultaneous localization and mapping framework named Dynamic-SLAM is proposed, in order to solve the problem of SLAM in dynamic environment. First, based on the convolutional neural network, an SSD object detector which combines prior knowledge is constructed to detect dynamic objects in the newly detection thread at semantic level. Then, in view of low recall rate of the existing SSD object detection network, a missed detection compensation algorithm based on the speed invariance in adjacent frames is proposed, which greatly improves the recall rate of detection. Finally, a feature-based visual SLAM system is constructed, which processes the feature points of dynamic objects through a selective tracking algorithm in the tracking thread, to significantly reduce the error of pose estimation caused by incorrect matching. The recall rate of the system is increased from 82.3% to 99.8% compared with the original SSD network. Several experiments show that the localization accuracy of Dynamic-SLAM is higher than the state-of-the-art systems. The system successfully localizes and constructs an accurate environmental map in real-world dynamic environment by using a mobile robot. In sum, our experimental demonstrations verify that Dynamic-SLAM shows improved accuracy and robustness in robot localization and mapping comparing to the state-of-the-art SLAM system in dynamic environment. (C) 2019 Elsevier B.V. All rights reserved.