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Policy-based optimization for matching validation algorithm in monocular robotics

机译:基于策略的单眼机器人匹配验证算法优化

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摘要

A novel policy to use the HOHCT validation algorithm is presented. HOHCT was introduced as a batch gating technique to validate data association in delayed initialization monocular SLAM. This new policy does not alter the inherent complexity of the algorithm, which lies in the exponential cost for worst case, but helps to keep it down to the average case-linear or quadratic-, while at the same time reducing the incidence of other operations with polynomial costs-obtaining new features for the map-. Statistics from off-line experiments with real data is use to evaluate costs and impact of the proposed policy.
机译:提出了一种使用HOHCT验证算法的新颖策略。引入HOHCT作为批处理门技术,以验证延迟初始化单眼SLAM中的数据关联。这一新策略不会改变算法的固有复杂性,而复杂性在于最坏情况下的指数成本,但有助于将其降低到线性或二次平均情况,同时降低了其他运算的发生率多项式成本-为地图获取新功能-离线实验的真实数据统计数据可用于评估成本和建议政策的影响。

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