Message Passing Least Squares Framework and its Application to Rotation Synchronization
This addresses robust rotation estimation in computer vision and robotics, but appears incremental as it builds on existing message passing and reweighted least squares approaches.
The paper tackles rotation synchronization under high corruption and noise by proposing a message passing algorithm to estimate corruption levels and a reweighted least squares method to estimate group elements, demonstrating superior performance over state-of-the-art methods on synthetic and real data.
We propose an efficient algorithm for solving group synchronization under high levels of corruption and noise, while we focus on rotation synchronization. We first describe our recent theoretically guaranteed message passing algorithm that estimates the corruption levels of the measured group ratios. We then propose a novel reweighted least squares method to estimate the group elements, where the weights are initialized and iteratively updated using the estimated corruption levels. We demonstrate the superior performance of our algorithm over state-of-the-art methods for rotation synchronization using both synthetic and real data.