TACO is a robust optimization framework for pose graph SLAM that combines online consistency testing with periodic sanitization using switchable constraints, showing high success rates on datasets with up to 50% outliers.
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TACO: A Test and Check Framework for Robust Pose Graph Optimization
TACO is a robust optimization framework for pose graph SLAM that combines online consistency testing with periodic sanitization using switchable constraints, showing high success rates on datasets with up to 50% outliers.