TL;DR
GDN-SLAM is presented, a geometry-guided dynamic SLAM framework that integrates point-line feature consistency, dual-stage dynamic feature suppression, and object-level neural scene constraints and improves localization accuracy and robustness over representative traditional and dynamic SLAM baselines, while maintaining practical runtime efficiency.
The proposed Semantic and Geometric Adaptive SLAM system effectively suppresses dynamic artifacts and point-cloud contamination in dense mapping, generating static environment maps with clearer structures and improved geometric consistency.
This paper presents a CPU-oriented dynamic point-line RGB-D SLAM method for robust localization in indoor scenes with moving objects, without relying on GPU acceleration. The method addresses two coupled challenges in dynamic RGB-D SLAM: corrupted visual constraints from moving foreground objects and weakened geometr...
Reliable localization and mapping are critical for intelligent robotic systems operating in dynamic indoor environments, where pedestrians and other moving objects can lead to erroneous feature associations, map contamination, and accumulated trajectory drift. To address these challenges, this study proposes the Bounda...
DynamicScore-VO is presented, a multi-cue dynamic-risk-aware front end that retains ORB extraction while assessing individual keypoints before ORB-SLAM2 Tracking, indicating that dynamic-feature filtering is not uniformly beneficial across motion regimes.
This work proposes Robust Semantic-aware Gaussian Splatting SLAM (RoSe-SLAM), to address the dynamic challenge by a holistic semantic scene understanding from uncalibrated monocular inputs, achieving accurate camera tracking and high-quality geometry reconstruction.
Stereo visual SLAM systems built on local descriptors suffer from semantic ambiguity, instance-level confusion, and independently moving objects, each corrupting data association and accumulating as trajectory drift. Prevailing semantic and dynamic SLAM methods address this through binary feature rejection, sacrificing...
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