5 papers · 1 filter
CU-Multi: A Dataset for Multi-Robot Collaborative Perception
Doncey Albin, Daniel McGann, Miles Mena +6
A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Collaborative SLAM (C-SLAM), benchma…
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,…
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…
iMESA: Incremental Distributed Optimization for Collaborative Simultaneous Localization and Mapping
Daniel McGann, Michael Kaess
This paper introduces a novel incremental distributed back-end algorithm for Collaborative Simultaneous Localization and Mapping (C-SLAM). For real-world deployments, robotic teams…