###### Abstract  
Multi-sensor fusion is pivotal in augmenting the robustness and precision of simultaneous localization and mapping (SLAM) systems. The LiDAR-visual-inertial approach has been empirically shown to adeptly amalgamate the benefits of these sensors for SLAM across various scenarios. Furthermore, methods of panoptic segmentation have been introduced to deliver pixel-level semantic and instance segmentation data in a single instance. This paper delves deeper into these methodologies, introducing PSMD-SLAM, a novel panoptic segmentation assisted multi-sensor fusion SLAM approach tailored for dynamic environments. Our approach employs both probability propagation-based and PCA-based clustering techniques, supplemented by panoptic segmentation. This is utilized for dynamic object detection and the removal of visual and LiDAR data, respectively. Furthermore, we introduce a module designed for the robust real-time estimation of the 6D pose of dynamic objects. We test our approach on a publicly available dataset and show that PSMD-SLAM outperforms other SLAM algorithms in terms of accuracy and robustness, especially in dynamic environments. SLAM; dynamic scenarios; panoptic segmentation; multi-sensor fusion +
Footnote †: journal:  
0000-0001-5000-0000-0001-5000][PERSON]  
1,2,1,6,8,9,and intricate environments in SLAM technology encompass interference from dynamic objects, complicating tracking and localization. Adverse conditions may degrade sensor performance, impacting data quality. The heightened complexity requires robust algorithms, elevating system complexity. Real-time processing is crucial, demanding timely feedback for successful SLAM task execution. Addressing these challenges necessitates advancements in algorithmic sophistication and sensor adaptability to enhance SLAM resilience in dynamic settings. One approach has attracted considerable interest in tackling these challenges: multi-sensor fusion SLAM. This method integrates data from various sensors, including vision, LiDAR, IMU (inertial measurement unit), and even WIFI signals [8], to improve system performance. LiDAR is an effective tool for navigation and tracking due to its high precision. However, it is constrained by weather conditions, a restricted detection range, and the requirement for color information. These factors can result in subpar loop closure detection in environments such as long corridors and tunnels. Conversely, vision sensors excel in textured environments but are hindered by their lackluster performance in low-texture scenes or rapidly changing illumination conditions. Monocular cameras also face challenges with scale degradation. Despite these limitations, IMU sensors offer high frequency and reliability, albeit with accuracy over short periods being somewhat limited. The potential of multi-sensor fusion SLAM lies in its ability to mitigate these constraints by amalgamating data from various sensors, thereby enhancing both accuracy and robustness. The second method integrates deep learning-driven semantic data with SLAM to enhance environmental comprehension. Dynamic SLAM systems that incorporate semantic segmentation, as exemplified by [9; 10; 11], leverage semantic information to augment the efficacy of SLAM in dynamic environments. Recent advancements in the realm of deep learning and computer hardware have propelled computers to achieve remarkable proficiency in classical image processing tasks. These tasks encompass image classification, object detection, and semantic segmentation. Panoptic segmentation represents an innovative approach that amalgamates these tasks, thereby facilitating pixel-level segmentation and object identification concurrently with object detection. This integration yields additional information for SLAM. This paper introduces PSMD-SLAM, an innovative SLAM method that incorporates panoptic segmentation-assisted multi-sensor fusion for dynamic environments.