Semantic-Independent Dynamic SLAM Based on Geometric Re-Clustering and Optical Flow Residuals

Dynamic objects pose significant challenges to the accuracy of state estimation and map quality in Simultaneous Localization and Mapping (SLAM). While current dynamic SLAM methods often rely on semantic information to detect specific movable objects, this dependency on pre-trained models and semantic priors can lead to false dynamic detections. This paper presents a novel semantic-independent dynamic SLAM method that detects truly moving regions, without being constrained by the classes or motion patterns of dynamic objects. We introduce a geometric re-clustering approach to improve object clustering by addressing the under- and over-segmentation caused by the K-Means algorithm. Next, instead of simply classifying entire clusters as dynamic or static, we propose a method to detect dynamic regions within each cluster based on dense optical flow residuals. This enables the detection of partial object movements, such as a seated person moving only his hands. Dynamic detection results are propagated across consecutive frames as dynamic priors for calculating optical flow residuals. Additionally, to enhance map quality, we address the mis-detection of slowly or intermittently moving objects through depth consistency checks applied over a larger time interval. Extensive evaluations on public datasets (TUM and Bonn) and real-world scenes show that our method outperforms state-of-the-art semantic-based methods in terms of localization accuracy and generalizability across various scenarios, particularly when facing unknown dynamic objects. Our method also achieves clean and dense reconstructions, demonstrating its potential for applications like robot navigation in dynamic environments.