论文标题
Stdyn-Slam：在动态室外环境中大满贯的立体视觉和语义细分方法
The STDyn-SLAM: A stereo vision and semantic segmentation approach for SLAM in dynamic outdoor environments
论文作者
论文摘要
通常，SLAM算法专注于静态环境，但是，在几个场景中都存在动态对象。这项工作为Stdyn-Slam提供了一个基于图像特征的大满贯系统，使用一系列子系系统（例如Optic Flow，Orb，Orb具有提取，视觉尾声和卷积神经网络）来辨别场景中的移动对象。神经网络用于支持对象检测和分割，以避免错误的地图和错误的系统定位。 STDYN-SLAM采用立体声对，是针对户外环境开发的。此外，所提出的系统的处理时间足够快，可以实时运行，因为它通过在实际动态室外环境中给出的实验证明了这一点。此外，我们将大满贯与最先进的方法进行了比较，从而实现了有希望的结果。
Commonly, SLAM algorithms are focused on a static environment, however, there are several scenes where dynamic objects are present. This work presents the STDyn-SLAM an image feature-based SLAM system working on dynamic environments using a series of sub-systems, like optic flow, orb features extraction, visual odometry, and convolutional neural networks to discern moving objects in the scene. The neural network is used to support object detection and segmentation to avoid erroneous maps and wrong system localization. The STDyn-SLAM employs a stereo pair and is developed for outdoor environments. Moreover, the processing time of the proposed system is fast enough to run in real-time as it was demonstrated through the experiments given in real dynamic outdoor environments. Further, we compare our SLAM with state-of-the-art methods achieving promising results.
