为微型机器人设计的稀疏测距定位算法，提升真实环境下的定位精度。
SoMaSLAM: 2D Graph SLAM for Sparse Range Sensing with Soft Manhattan World Constraints
- 用软曼哈顿世界约束融合地标间关系，适应非规则真实场景。
- 在多个数据集上定位误差显著降低，长期运行漂移更小。
- 适合资源受限的微型无人机或移动机器人实时定位使用。
针对微型机器人稀疏测距的挑战，我们提出SoMaSLAM，一种2D图优化式SLAM算法。传统方法因数据点稀疏导致定位漂移严重，而基于严格曼哈顿世界（Manhattan World, MW）的方案在真实环境中表现不佳。本文将MW假设转化为软约束，引入新型地标-地标软约束机制，使系统能自适应复杂环境。实验表明，该方法在多样数据集上显著提升定位精度，同时保持对真实世界的鲁棒性。代码与数据集已开源：https://SoMaSLAM.github.io/。
原文摘要 · Abstract (English)
We propose a graph SLAM algorithm for sparse range sensing that incorporates a soft Manhattan world utilizing landmark-landmark constraints. Sparse range sensing is necessary for tiny robots that do not have the luxury of using heavy and expensive sensors. Existing SLAM methods dealing with sparse range sensing lack accuracy and accumulate drift error over time due to limited access to data points. Algorithms that cover this flaw using structural regularities, such as the Manhattan world (MW), have shortcomings when mapping real-world environments that do not coincide with the rules. We propose SoMaSLAM, a 2D graph SLAM designed for tiny robots with sparse range sensing. Our approach effectively maps sparse range data without enforcing 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 on diverse datasets and is flexible enough to be used in the real world. We release our source code and sparse range datasets at https://SoMaSLAM.github.io/.
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