strict structural regularities and maintains an adaptive graph. We implement the MW assumption as soft constraints, which we refer to as a soft Manhattan world. We propose novel soft landmark-landmark constraints to incorporate the soft MW into graph SLAM. Through extensive evaluation, we demonstrate that our proposed SoMaSLAM method improves localization accuracy across diverse datasets and is flexible enough to be used in the real world. We release our source code and dataset on our project page https://SoMaSLAM.github.io/.">

SoMaSLAM: 2D Graph SLAM for Sparse Range Sensing With Soft Manhattan World Constraints (original) (raw)

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