Global localization in SLAM in bilinear time
Global localization in SLAM in bilinear time
复制标题
双线性时间内 SLAM 的全局定位
DOI：
10.1109/iros.2005.1545055
复制
发表时间：
2005
期刊：
影响因子：
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通讯作者：
J. D. Tardós
中科院分区：
文献类型：
--
作者：
L. Paz;P. Piniés;José Neira;J. D. Tardós
本文研究了SLAM中的全局定位问题：在没有其他先验信息的情况下，在预先绘制的环境中确定车辆的位置。我们表明，使用配置空间的网格采样表示，可以评估环境中所有车辆位置假设（达到一定分辨率），计算成本是双线性的：地图特征的数量和传感器测量的数量都是线性的。我们提出了一种配对驱动算法，它只考虑单个的测量特征配对，因此，与现有的对应空间算法相比，它避免了在指数对应空间中搜索。它使用投票策略，为每个车辆位置假设积累证据，确保传感器测量和环境模型对噪声的鲁棒性。所提出的策略的一般性质允许考虑不同类型的特征和传感器测量。使用流行的维多利亚公园数据集，我们将其性能与位置驱动算法进行比较，其中解空间通常是随机采样的。我们表明，所提出的配对驱动技术的计算效率与环境中特征的密度成正比。
In this paper we study the global localization problem in SLAM: the determination of the vehicle location in a previously mapped environment with no other prior information. We show that, using a grid sampling representation of the configuration space, it is possible to evaluate all vehicle location hypotheses in the environment (up to a certain resolution) with a computational cost that is bilinear: linear both in the number of map features and in the number of sensor measurements. We propose a pairing-driven algorithm that considers only individual measurement-feature pairings and thus, in contrast with current correspondence space algorithms, it avoids searching in the exponential correspondence space. It uses a voting strategy that accumulates evidence for each vehicle location hypothesis, assuring robustness to noise in the sensor measurements and environment models. The general nature of the proposed strategy allows the consideration of different types of features and sensor measurements. Using the popular Victoria Park dataset, we compare its performance with location-driven algorithms where the solution space is usually randomly sampled. We show that the proposed pairing-driven technique is computationally more efficient in proportion to the density of features in the environment.