CAOS-SLAM: Class-Agnostic Object-Aware Real-Time vSLAM for Dynamic Environments

Key points

• 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.1
• On-demand panoptic segmentation is performed for object discovery, with candidate unknown objects instantiated through depth-based clustering of unclassified regions.1
• 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.1
• 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.1

Sources (1)

• [1]CAOS-SLAM: Class-Agnostic Object-Aware Real-Time vSLAM for Dynamic Environments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:00 PM

  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.

  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.

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