Improved simultaneous localization and mapping algorithm combined with semantic segmentation model

• Published in: International journal of distributed sensor networks Vol. 17; no. 4; p. 155014772110141
• Main Authors: Cui, Xuerong, Xue, Shengjie, Li, Juan, Li, Shibao, Liu, Jianhang, Chen, Haihua
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.04.2021; Wiley
• Subjects: Dynamic scene; simultaneous localization and mapping; semantic segmentation; visual positioning
• ISSN: 1550-1329; 1550-1477; 1550-1477
• DOI: 10.1177/15501477211014131

In the past decades, emerging technologies such as unmanned driving and indoor navigation have developed rapidly, and simultaneous localization and mapping has played unparalleled roles as core technologies. However, dynamic objects in complex environments will affect the positioning accuracy. In order to reduce the influence of dynamic objects, this article proposes an improved simultaneous localization and mapping algorithm combined with semantic segmentation model. First, in the pre-processing stage, in order to reduce the influence of dynamic features, fully convolutional network model is used to find the dynamic object, and then the output image is masked and fused to obtain the final image without dynamic object features. Second, in the feature-processing stage, three parts are improved to reduce the computing complexity, which are extracting, matching, and eliminating mismatching feature points. Experiments show that the absolute trajectory accuracy in high dynamic scene is improved by 48.58% on average. Meanwhile, the average processing time is also reduced by 21.84%.