C-KLAM : Constrained Keyframe Localization and Mapping for Long-Term Navigation
C-KLAM : Constrained Keyframe Localization and Mapping for Long-Term Navigation
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C-KLAM：用于长期导航的约束关键帧定位和映射
DOI：
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发表时间：
2013
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通讯作者：
S. Roumeliotis
中科院分区：
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作者：
Esha D. Nerurkar;Kejian Wu;S. Roumeliotis
在本文中，我们提出了C-KLAM，最大后验概率（MAP）估计为基础的SLAM关键帧的方法。与许多现有的基于关键帧的SLAM方法相反，这些方法丢弃来自非关键帧的信息以降低计算复杂度，所提出的C-KLAM提出了一种新颖且计算高效的技术，用于合并大部分这些信息，从而提高估计精度。具体来说，C-KLAM使用边缘化将信息从非关键帧投影到关键帧，同时保持信息矩阵的稀疏结构，以生成快速有效的解决方案。C-KLAM的性能已经在模拟和实验中进行了测试，使用视觉和惯性测量，以证明它实现了与使用所有可用测量信息的计算密集型批量MAP的3D SLAM相当的性能。
In this paper, we present C-KLAM, a Maximum A Posteriori (MAP) estimator-based keyframe approach for SLAM. As opposed to many existing keyframe-based SLAM approaches, that discard information from non-keyframes in order to reduce the computational complexity, the proposed C-KLAM presents a novel and computationally-efficient technique for incorporating most of this information, resulting in improved estimation accuracy. Specifically, C-KLAM projects information from the non-keyframes to the keyframes, using marginalization, while maintaining the sparse structure of the information matrix, to generate fast and efficient solutions. The performance of C-KLAM has been tested in both simulations and experimentally, using visual and inertial measurements, to demonstrate that it achieves performance comparable to that of the computationally-intensive batch MAP-based 3D SLAM that uses all available measurement information.