SLAM with single cluster PHD filters
SLAM with single cluster PHD filters
复制标题
具有单簇 PHD 滤波器的 SLAM
DOI：
10.1109/icra.2012.6224953
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
J. Salvi
中科院分区：
文献类型：
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作者：
Chee Sing Lee;Daniel E. Clark;J. Salvi
Mullane、Vo和亚当斯最近的工作重新研究了基于特征的同时定位和地图构建（SLAM）的概率基础，将问题归结为随机有限集的过滤问题。算法是基于概率假设密度（PHD）滤波技术开发的，该技术在具有高误报率、高漏检率和高测量噪声水平的挑战性测量场景中为领先的基于特征的SLAM算法提供了上级性能。我们进一步研究这种方法，通过考虑一个分层点过程，或单簇多对象，模型，在那里我们认为状态由一个地图的地标条件下的车辆状态。使用有限集统计，我们能够找到一个简单的公式来近似联合车辆地标状态的基础上，一个单一的泊松多目标假设的预测密度。我们描述了单簇PHD过滤器和实际的实施开发的基础上的粒子系统表示的车辆状态和高斯混合近似的地图为每个粒子。合成仿真结果比较新的算法与以前的PHD滤波器SLAM算法。结果表明，车辆和地图地标定位的上级性能，地标基数估计的性能相当。
Recent work by Mullane, Vo, and Adams has re-examined the probabilistic foundations of feature-based Simultaneous Localization and Mapping (SLAM), casting the problem in terms of filtering with random finite sets. Algorithms were developed based on Probability Hypothesis Density (PHD) filtering techniques that provided superior performance to leading feature-based SLAM algorithms in challenging measurement scenarios with high false alarm rates, high missed detection rates, and high levels of measurement noise. We investigate this approach further by considering a hierarchical point process, or single-cluster multi-object, model, where we consider the state to consist of a map of landmarks conditioned on a vehicle state. Using Finite Set Statistics, we are able to find tractable formulae to approximate the joint vehicle-landmark state based on a single Poisson multi-object assumption on the predicted density. We describe the single-cluster PHD filter and the practical implementation developed based on a particle-system representation of the vehicle state and a Gaussian mixture approximation of the map for each particle. Synthetic simulation results are presented to compare the novel algorithm against the previous PHD filter SLAM algorithm. Results presented indicate a superior performance in vehicle and map landmark localization, and comparable performance in landmark cardinality estimation.