# The STDyn-SLAM: A stereo vision and semantic segmentation approach for SLAM in dynamic outdoor environments Source: [The STDyn-SLAM: A stereo vision and semantic segmentation approach for SLAM in dynamic outdoor environments]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:03:46.301Z Authors: Daniela Esparza, Gerardo Flores Published: 2020-10-19T20:50:12.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()