Stereo SLAM in Dynamic Environments Using Semantic Segmentation

Abstract

As we all know, many dynamic objects appear almost continuously in the real world that are immensely capable of impairing the performance of the majority of vision-based SLAM systems based on the static-world assumption. In order to improve the robustness and accuracy of visual SLAM in high-dynamic environments, a real-time and robust stereo SLAM system for dynamic scenes was proposed. To weaken the influence of dynamic content, the moving-object detection method was put forward in our visual odometry, and then the semantic segmentation network was combined in our stereo SLAM to extract pixel-level contours of dynamic objects. Then, the influences of dynamic objects were significantly weakened and the performance of our system increased markedly in dynamic, complex, and crowed city spaces. Following experiments with both the KITTI Odometry dataset and in a real-life scene, the results showed that our method could dramatically decrease the tracking error or drift, and improve the robustness and stability of our stereo SLAM in high dynamic outdoor scenarios.

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

“…The reliance on visual cues alone can lead to errors in depth estimation and spatial understanding, especially in environments with limited visual features or occlusions. Other explorations in the visual SLAM field have adopted machine learning approaches utilizing YOLO, convolutional neural networks, and semantic segmentation, among others [7,[16][17][18]. In light of the limitations of visual SLAM, 3D SLAM emerges as a superior solution, albeit with certain drawbacks, particularly in terms of labour intensity.…”