Efficient Map Prediction via Low-Rank Matrix Completion
Efficient Map Prediction via Low-Rank Matrix Completion
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DOI：
10.1109/icra48506.2021.9561353
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发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Zheng Chen;Shi Bai;Lantao Liu
中科院分区：
文献类型：
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作者：
Zheng Chen;Shi Bai;Lantao Liu
在许多自主测绘任务中，由于诸如稀疏、噪声和部分传感器测量的各种原因，地图不能被准确地构建。我们提出了一种新的地图预测方法建立在最近成功的低秩矩阵完成。所提出的地图预测是能够实现地图内插和外推的原始质量差的地图与丢失或噪声观测。通过大量的仿真实验，验证了该方法能够实现大地图的实时计算，并且在映射精度和计算时间方面优于最先进的地图预测方法--贝叶斯希尔伯特映射（Bayesian Hilbert Mapping），具有上级的性能。然后，我们证明，与建议的实时地图预测框架，覆盖收敛速度（每个动作步骤）的一组代表性的覆盖规划方法，通常用于环境建模和监测任务，可以显着提高。
In many autonomous mapping tasks, the maps cannot be accurately constructed due to various reasons such as sparse, noisy, and partial sensor measurements. We propose a novel map prediction method built upon recent success of Low-Rank Matrix Completion. The proposed map prediction is able to achieve both map interpolation and extrapolation on raw poor-quality maps with missing or noisy observations. We validate with extensive simulated experiments that the approach can achieve real-time computation for large maps, and the performance is superior to state-of-the-art map prediction approach — Bayesian Hilbert Mapping in terms of mapping accuracy and computation time. Then we demonstrate that with the proposed real-time map prediction framework, the coverage convergence rate (per action step) for a set of representative coverage planning methods commonly used for environmental modeling and monitoring tasks can be significantly improved.