The STDyn-SLAM: A Stereo Vision and Semantic Segmentation Approach for VSLAM in Dynamic Outdoor Environments

The Visual Simultaneous Localization and Mapping (VSLAM) is a system based on the scene’s features to estimate a map and the system pose. Commonly, VSLAM algorithms are focused on a static environment; however, some dynamic objects are present in the vast majority of real-world applications. This work presents a feature-based SLAM system focused on dynamic environments using convolutional neural networks, optical flow, and depth maps to detect objects in the scene. The proposed system employs a stereo camera as the primary sensor to capture the scene. The neural network is responsible for object detection and segmentation to avoid erroneous maps and wrong system locations. Moreover, the proposed system’s processing time is fast and can run in real-time, running in outdoor and indoor environments. The proposed approach has been compared with state-of-the-art; besides, we present several experimental results outdoors that corroborate the approach’s effectiveness. Our code is available online.