Bathymetric particle filter SLAM using trajectory maps
Bathymetric particle filter SLAM using trajectory maps
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DOI：
10.1177/0278364912459666
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发表时间：
2012-10-01
影响因子：
9.2
通讯作者：
Jakuba, Michael V.
中科院分区：
文献类型：
--
作者：
Barkby, Stephen;Williams, Stefan B.;Jakuba, Michael V.
我们提出了一种高效而无特征的同时定位和测绘(SLAM)方法，该方法利用Rao-Blackwell粒子滤波(RBPF)和高斯过程(GP)回归来在与先前探索的地形几乎没有重叠的区域提供环路闭合。为了显著减少内存需求(从而允许处理大型数据集)，还引入了一种新的地图表示法，它不是直接存储海底深度的估计值，而是记录每个颗粒的轨迹，并将其与共同的测深观测日志同步。在检测到环路闭合时，通过将新的观测与使用GP回归对其地图的局部重建生成的当前预测进行匹配来对每个粒子进行加权。在这里，充分利用了环境中的空间相关性，从而能够预测以前可能没有直接观测到的地区的海底深度。结果表明，与航位推算与长基线(LBL)观测相比，测深SLAM如何利用部分重叠的海底结构观测来提高地图的自我一致性。此外，我们还展示了即使在不存在地图重叠的情况下，仍然可以实现地图校正。
We present an efficient and featureless approach to bathymetric simultaneous localization and mapping (SLAM) that utilizes a Rao-Blackwellized particle filter (RBPF) and Gaussian process (GP) regression to provide loop closures in areas with little to no overlap with previously explored terrain. To significantly reduce the memory requirements (thereby allowing for the processing of large datasets) a novel map representation is also introduced that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronizes them to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions generated from a local reconstruction of their map using GP regression. Here the spatial correlation in the environment is fully exploited, allowing predictions of seabed depth in areas that may not have been directly observed previously. The results demonstrate how observations of seafloor structure with partial overlap can be used by bathymetric SLAM to improve map self consistency when compared with dead reckoning fused with long-baseline (LBL) observations. In addition we show how mapping corrections can still be achieved even when no map overlap is present.