4 papers
riMESA: Consensus ADMM for Real-World Collaborative SLAM
Daniel McGann, Michael Kaess
Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for multi-robot teams as it enables downstream tasks like planning and navigation. However,…
GrndCtrl: Grounding World Models via Self-Supervised Reward Alignment
Haoyang He, Jay Patrikar, Dong-Ki Kim +5
Recent advances in video world modeling have enabled large-scale generative models to simulate embodied environments with high visual fidelity, providing strong priors for predicti…
FORM: Fixed-Lag Odometry with Reparative Mapping utilizing Rotating LiDAR Sensors
Easton R. Potokar, Taylor Pool, Daniel McGann +1
Light Detection and Ranging (LiDAR) sensors have become a de-facto sensor for many robot state estimation tasks, spurring development of many LiDAR Odometry (LO) methods in recent…
COSMO-Bench: A Benchmark for Collaborative SLAM Optimization
Daniel McGann, Easton R. Potokar, Michael Kaess
Recent years have seen a focus on research into distributed optimization algorithms for multi-robot Collaborative Simultaneous Localization and Mapping (C-SLAM). Research in this d…