Efficient Map Prediction via Low-Rank Matrix Completion

TL;DR

This paper introduces a low-rank matrix completion-based method for real-time map prediction that improves accuracy and efficiency in autonomous mapping tasks with noisy or incomplete data.

Contribution

It presents a novel, real-time map prediction framework leveraging low-rank matrix completion, outperforming existing methods in accuracy and computational speed.

Findings

Achieves superior mapping accuracy compared to Bayesian Hilbert Mapping.

Enables real-time processing of large maps.

Significantly improves coverage convergence rate in environmental monitoring.

Abstract

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 the 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 the 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.