# Efficient Map Prediction via Low-Rank Matrix Completion

arXiv: https://arxiv.org/abs/2111.00075 · HTML: https://mlsanity.com/paper/2111.00075/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2021.

## Built on by (most cited first, 2 of 2)

- [4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration](https://mlsanity.com/paper/2402.17904/) (2024)
- [SePaint: Semantic Map Inpainting via Multinomial Diffusion](https://mlsanity.com/paper/2303.02737/) (2023)

## Abstract (arXiv)

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.