DC-SLAM: dynamic prior update and missed detection compensation for robust RGB-D SLAM

Abstract Simultaneous Localization and Mapping (SLAM) is a fundamental technology used for robotic perception and navigation. Conventional SLAM systems assume static environments, which often fails in real-world dynamic scenarios where moving objects cause significant errors or tracking failures. Current approaches to dynamic feature filtering primarily rely on either multi-view geometry or deep learning techniques. While geometry-based approaches are computationally intensive and environmentally sensitive, deep learning-based methods are constrained by fixed priors, making them less effective at handling pseudo-static objects and prone to missed detections under occlusion or motion blur. To overcome these limitations, we present DC-SLAM, a framework that synergistically combines geometric constraints with deep learning to enable both accurate dynamic prior updates and robust missed-detection compensation in real-time operation. Its key innovations include, first, accurately identifying pseudo-static objects using a dynamic reference frame comparison algorithm and decoupling their motion from the background via depth information and multi-view geometric constraints, enabling real-time prior updates. Second, to address missed detections from sudden viewpoint changes, motion blur, or occlusions, it predicts target positions through spatiotemporal motion consistency analysis and locates compensation boxes within expanded regions via a column-wise depth clustering algorithm, greatly improving robustness. To evaluate the proposed algorithm, comprehensive experiments are conducted on the TUM RGB-D benchmark against state-of-the-art SLAM systems, along with real-world tests in dynamic environments. On four TUM RGB-D Fr3 dynamic sequences, DC-SLAM attains the lowest ATE across all methods, with 93%–98% lower ATE RMSE than ORB-SLAM3 and up to 80% lower than recent dynamic-aware baselines including DS-SLAM, RDS-SLAM, and SG-SLAM. Real-world mobile robot experiments further demonstrate DC-SLAM’s capability to reliably filter dynamic objects and maintain precise localization, while exhibiting exceptional robustness against challenging conditions such as viewpoint variations and occlusions, ultimately delivering a highly dependable visual SLAM solution for dynamic environments.