Efficient Map Prediction via Low-Rank Matrix Completion
This improves autonomous mapping for robotics applications, though it appears incremental as it builds on existing low-rank matrix completion techniques.
The paper tackles the problem of inaccurate map construction from sparse, noisy sensor data by proposing a low-rank matrix completion method for map prediction, achieving superior mapping accuracy and real-time computation compared to Bayesian Hilbert Mapping.
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.