论文标题
稀疏高斯流程专家的合奏，用于与流数据隐式地面映射
Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data
论文作者
论文摘要
创建地图是机器人技术的重要任务，为有效的计划和导航提供了基础。在本文中，我们从具有已知姿势的范围数据流中学习了一个紧凑而连续的隐式表面图。为此，我们创建并逐步调整了近似高斯流程（GP）专家的集合，这些集合每个人都负责地图的不同部分。我们没有将所有到达数据插入GP模型中，而是在模型复杂性和预测错误之间进行了折磨。因此，我们的算法在几何特征和环境丰富的多样性的区域上使用的资源较少。我们评估了关于合成和现实世界数据集的方法，并分析对参数和测量噪声的敏感性。结果表明，在不同条件下，我们可以学习紧凑而准确的隐式表面模型，其性能与使用亚采样数据相当或更好的GP回归。
Creating maps is an essential task in robotics and provides the basis for effective planning and navigation. In this paper, we learn a compact and continuous implicit surface map of an environment from a stream of range data with known poses. For this, we create and incrementally adjust an ensemble of approximate Gaussian process (GP) experts which are each responsible for a different part of the map. Instead of inserting all arriving data into the GP models, we greedily trade-off between model complexity and prediction error. Our algorithm therefore uses less resources on areas with few geometric features and more where the environment is rich in variety. We evaluate our approach on synthetic and real-world data sets and analyze sensitivity to parameters and measurement noise. The results show that we can learn compact and accurate implicit surface models under different conditions, with a performance comparable to or better than that of exact GP regression with subsampled data.
