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The paper targets robot localization in moving scenes, combining panoptic segmentation, depth clustering, and learned detection with classical ORB-SLAM3 and Kalman filtering. Its reported gains are largest when dynamic objects are unseen or heavily occluded, which is where class-agnostic perception matters most. Readers should weigh the claimed accuracy and 30 FPS results against the promised but not yet released code and dataset.
CAOS-SLAM: Class-Agnostic Object-Aware Real-Time vSLAM for Dynamic Environments
arXiv:2610.26633v1 Announce Type: new Abstract: Robust visual Simultaneous Localization and Mapping (vSLAM) in dynamic environments faces challenges due to the presence of moving objects that may introduce erroneous observations, corrupt the map reconstruction and overall degrade localization accuracy. Existing vSLAM approaches often rely on predefined semantic classes or costly pixel-wise classification, limiting their robustness or applicability in real-world scenarios. This paper presents CAOS-SLAM, a dynamic vSLAM framework that extends ORB-SLAM3 with a hybrid perception pipeline to detect, track, and filter both semantically known and previously unseen moving objects in real-time. On-demand panoptic segmentation is performed for object discovery, with candidate unknown objects instantiated through depth-based clustering of unclassified regions. Between panoptic updates, persistent object tracks are updated using lightweight detection when available or temporal mask propagation based on world-frame Kalman prediction, depth consistency, and appearance similarity. The estimated object motion is then used to reject dynamic features from the SLAM pipeline. Experimental results on multiple dynamic RGB-D datasets and real-world experiments demonstrate improved localization accuracy over state-of-the-art dynamic vSLAM approaches, achieving up to 59% lower localization error in scenes characterized by unknown dynamic objects and up to 88% lower error in heavily occluded scenes, while achieving a median processing rate of 30 FPS on a laptop. Furthermore, the tracking module provides estimates of object trajectories and velocities, supporting potential future applications in perception, navigation, and motion-planning tasks in dynamic environments. Code and dataset will be released upon acceptance.
Abstract:Robust visual Simultaneous Localization and Mapping (vSLAM) in dynamic environments faces challenges due to the presence of moving objects that may introduce erroneous observations, corrupt the map reconstruction and overall degrade localization accuracy. Existing vSLAM approaches often rely on predefined semantic classes or costly pixel-wise classification, limiting their robustness or applicability in real-world scenarios. This paper presents CAOS-SLAM, a dynamic vSLAM framework that extends ORB-SLAM3 with a hybrid perception pipeline to detect, track, and filter both semantically known and previously unseen moving objects in real-time. On-demand panoptic segmentation is performed for object discovery, with candidate unknown objects instantiated through depth-based clustering of unclassified regions. Between panoptic updates, persistent object tracks are updated using lightweight detection when available or temporal mask propagation based on world-frame Kalman prediction, depth consistency, and appearance similarity. The estimated object motion is then used to reject dynamic features from the SLAM pipeline. Experimental results on multiple dynamic RGB-D datasets and real-world experiments demonstrate improved localization accuracy over state-of-the-art dynamic vSLAM approaches, achieving up to 59% lower localization error in scenes characterized by unknown dynamic objects and up to 88% lower error in heavily occluded scenes, while achieving a median processing rate of 30 FPS on a laptop. Furthermore, the tracking module provides estimates of object trajectories and velocities, supporting potential future applications in perception, navigation, and motion-planning tasks in dynamic environments. Code and dataset will be released upon acceptance.
Robotics (cs.RO)
arXiv:2610.26633 [cs.RO]
(or arXiv:2610.26633v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.26633 Focus to learn more arXiv-issued DOI via DataCite
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