SIMSL: A Semantic Instance-Based Multisensor Fusion Localization System for Challenging Dynamic and Degraded Environments

To address the localization challenges in indoor dynamic and degraded environments such as long corridors with sparse and repetitive features, we present a semantic instance-based multisensor fusion localization (SIMSL) system. We introduce a multistrategy corner classification model that achieves accurate corner classification by integrating multisensor data. We combine Bayesian filtering with semantic segmentation to build a semantic instance map enriched with corner information and build a semantic dimension chain using a path exploration model. In terms of localization, we propose a robust semantic dimension chain matching algorithm, which, when integrated with scan-to-map, enables millisecond-level precise global localization even in dynamic and degraded environments. Additionally, our multisensor cumulative error compensation and kidnapping detection mechanisms effectively eliminate the cumulative errors from odometry and IMU while accurately detecting localization anomalies and restoring pose. Extensive localization experiments conducted in various real-world environments demonstrate that our proposed algorithm outperforms existing methods. Compared to a state-of-the-art algorithm, the global localization success rate improved by 12.51%, and the average localization time decreased by 52.19%.