Ai, Y.; Sun, Q.; Xi, Z.; Li, N.; Dong, J.; Wang, X. Stereo SLAM in Dynamic Environments Using Semantic Segmentation. Electronics2023, 12, 3112.
Ai, Y.; Sun, Q.; Xi, Z.; Li, N.; Dong, J.; Wang, X. Stereo SLAM in Dynamic Environments Using Semantic Segmentation. Electronics 2023, 12, 3112.
Ai, Y.; Sun, Q.; Xi, Z.; Li, N.; Dong, J.; Wang, X. Stereo SLAM in Dynamic Environments Using Semantic Segmentation. Electronics2023, 12, 3112.
Ai, Y.; Sun, Q.; Xi, Z.; Li, N.; Dong, J.; Wang, X. Stereo SLAM in Dynamic Environments Using Semantic Segmentation. Electronics 2023, 12, 3112.
Abstract
As we all know, many more dynamic objects appear almost continuously in real world, which are immensely able to impair the performance of the majority of vision-based SLAM systems that are based on the static-world assumption. In order to improve the robustness and accuracy of visual SLAM in the high dynamic environment, a real-time and robust stereo SLAM system for dynamic scenes was proposed. To weaken influences of dynamic content, the moving object detection method was put forward in our visual odometry, then the semantic segmentation network was combined into our stereo SLAM to extract pixel-level contours of dynamic objects. Then influences of dynamic objects were extremely weaken and the performance of our system was increased markedly in dynamic, complex and crowed city spaces. Experiment with both on KITTI Odometry dataset and in a real-life scene, the results show that our method can dramatically decrease the tracking error or drift, improve the robustness and stability of our stereo SLAM in high dynamic outdoor scenarios.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
