The STDyn-SLAM: A stereo vision and semantic segmentation approach for SLAM in dynamic outdoor environments
This work addresses SLAM for dynamic outdoor scenes, which is an incremental improvement over static environment methods.
The authors tackled the problem of SLAM in dynamic outdoor environments by integrating stereo vision, semantic segmentation, and object detection to filter moving objects, achieving real-time performance and promising results compared to state-of-the-art methods.
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.