EMPNet: Neural Localisation and Mapping Using Embedded Memory Points，2019
EMPNet: Neural Localisation and Mapping Using Embedded Memory Points，2019 澳大利亚莫纳什大学，定位和建图 作者使用CNN作为底层的特征提取器，然后通过计算距离矩阵来进行对应点匹配，然后执行最优化算法，将观测点融合到记忆数据中。但是距离矩阵的计算复杂度是线性的，意味这算法实时性只有在短期记忆时能够的得以保证。 总结： 作者指出
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相关文章
- 1. Localization and Mapping using Instance-specific Mesh Models 2019
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- 5. Memory and unicode
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- 10. Attention and Memory in Deep Learning and NLP